SHAPING SUSTAINABLE VEHICLE FLEET CONVERSION POLICIES BASED ON LIFE CYCLE OPTIMIZATION AND RISK ANALYSIS
by
Hyung Chul Kim
A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy (Natural Resources and Environment) in The University of Michigan 2003
Doctoral Committee:
Associate Professor Gregory A. Keoleian, Co-Chair Professor Jonathan W. Bulkley, Co-Chair Professor James C. Bean Professor Marc H. Ross James H. Lindner, US EPA
Hyung Chul Kim © 2003 All Rights Reserved
for my parents
ii ACKNOWLEDGMENTS
First and foremost, I would like to offer my sincerest thanks to my University of Michigan committee members: to my co-chair, Professor Gregory Keoleian, for his encouragement and careful and wise guidance of my project; to my co-chair, Professor Jonathan Bulkley, for his kind advice and academic support; to Professor Marc Ross for always answering my inquiries so thoughtfully and promptly; and to Professor James Bean for his time and help in solving technical problems. In addition, Dr. Jim Lindner at the US Environmental Protection Agency provided me with invaluable information and direction. This research would have never been completed without support from outside the university. I am deeply grateful to Kevin Cullen, Terry Cullum, and Ronald Williams at General Motors for providing me with both technical and financial support in the early stages of this research. Special gratitude is also extended to John Walls at the Arizona Department of Environmental Quality for the essential data and kind answers to my questions. I thank Ruth Kraut for her advice throughout the writing process. Her help dramatically improved this dissertation. Many thanks are also due to Darby Grande for her suggestions and for other help during the modeling process. I have been helped and encouraged by many people at the Center for Sustainable Systems at the School of Natural Resources and Environment in one way or another. I would like to single out several people for special thanks: Martin Heller, Helaine Hunscher, and Marc Melaina.
iii Finally, I thank all of my friends in Ann Arbor, who have been with me throughout this critical period in my life.
iv TABLE OF CONTENTS
DEDICATION...... ii
ACKNOWLEDGMENTS ...... iii
LIST OF FIGURES ...... viii
LIST OF TABLES ...... xi
LIST OF APPENDICES ...... xiii
CHAPTER
I. INTRODUCTION...... 1 1.1 High-Emitters...... 3 1.2 Scrappage Program ...... 5 1.3 Inspection and Maintenance (I/M) Programs ...... 8 1.4 Problem Statements ...... 12 1.5 Dissertation Outline ...... 14 II. DYNAMIC LIFE CYCLE INVENTORY...... 17 2.1 Vehicle LCA ...... 17 2.2 Dynamic LCI Conceptual Structure...... 19 2.3 Dynamic LCI Modeling for a Generic Vehicle ...... 22 2.3.1 Materials Production...... 24 2.3.2 Manufacturing...... 25 2.3.3 Use ...... 26 2.3.4 Maintenance...... 28 2.3.5 End-of-Life ...... 29 2.4 Dynamic LCI Parameters for a Generic Vehicle ...... 30 2.4.1 Recycled Content...... 30 2.4.2 Materials Use ...... 33 2.4.3 Vehicle Miles Traveled (VMT) ...... 34 2.4.4 Energy Intensity...... 34 2.4.5 Fuel Economy ...... 45 2.4.6 Emission Factors...... 47 2.4.7 Component Reliability...... 54 2.5 Results and Discussion ...... 55 2.6 Conclusion ...... 62
v III. OPTIMAL VEHICLE LIFE BASED ON LIFE CYCLE OPTIMIZATION MODEL...... 63 3.1 Introduction...... 63 3.2 Life Cycle Optimization (LCO) Model ...... 64 3.2.1 Dynamic Programming...... 64 3.2.2 Model Construction ...... 65 3.2.3 Model Application ...... 69 3.3 Results and Discussion ...... 70 3.3.1 Optimal Lifetimes for Mid-Sized Cars ...... 70 3.3.2 Determinants of Optimal Lifetime...... 73 3.3.3 Policy Implications ...... 76 3.4 Conclusion ...... 78 IV. OPTIMAL VEHICLE LIFE BASED ON FLEET OPTIMIZATION MODEL ...... 80 4.1 Introduction...... 80 4.2 Fleet Characterization ...... 81 4.3 Sources of Emissions ...... 84 4.4 Fleet Optimization Modeling...... 85 4.4.1 Baseline Fleet...... 85 4.4.2 Fleet Optimization Scheme...... 87 4.4.3 Mathematical Modeling...... 88 4.5 Results...... 90 4.5.1 Ideal Fleet Conversion ...... 91 4.5.2 Long-Term Fleet Conversion...... 92 4.5.3 Emission Reductions...... 94 4.5.4 Multi-Objective Analysis...... 94 4.5.5 Sensitivity of Results ...... 98 4.6 Discussion...... 99 4.6.1 Fleet Conversion Policy...... 99 4.6.2 Multi-Objective Analysis...... 101 4.7 Conclusion ...... 102 V. IMPROVING INSPECTION AND MAINTENANCE PROGRAMS USING RISK ANALYSIS OF EMISSION CONTROL SYSTEMS ...... 104 5.1 Introduction...... 104 5.2 Fault Tree Analysis of Emission Control Systems ...... 106 5.2.1 Emission Control Systems ...... 108 5.3 Analysis of IM147 Records ...... 116 5.3.1 Overview of the Arizona IM147 Tests ...... 116 5.3.2 Overview of I/M Repairs ...... 122 5.3.3 Repairs of CO High-Emitters...... 135 5.3.4 Repairs of NOx High-Emitters ...... 137 5.3.5 Repairs of HC/CO High-Emitters...... 140 5.3.6 Repairs of HC/CO/NOx High-Emitters...... 144 5.3.7 I/M Analysis Based on Manufacturers ...... 146
vi 5.4 Emission Reductions through I/M Program ...... 148 5.4.1 Cars Tested During both 2000 and 2002 I/M Cycles...... 149 5.4.2 All Cars Tested ...... 151 5.5 High-Emitter Contributions to Fleet Emissions...... 155 5.6 I/M versus Scrappage Policy Examinations ...... 157 5.7 Scrappage Policies from a Life Cycle Perspective ...... 161 5.8 Conclusion ...... 169 VI. CONCLUSION ...... 172 6.1 Research Scope and Key Findings...... 172 6.2 Policy Implications ...... 175 6.3 Future Research ...... 177 APPENDICES...... 179
BIBLIOGRAPHY...... 221
vii LIST OF FIGURES
Figure
2.1. Life cycle energy consumption of a generic mid-sized passenger vehicle based on 120,000 miles of driving (USAMP 1999)...... 20
2.2. Factors, parameters, and life cycle stages illustrating the dynamic LCI for a generic vehicle...... 21
2.3. The detailed dependencies between the parameters and environmental burdens of the use phase life cycle ...... 27
2.4. Fuel economy trends and forecast between 1985 and 2020: (EPA 2000; EIA 2001) ...... 47
2.5. Estimation of the average (at 80K miles) emission factor contributions * of high-emitters (E H) (Austin and Ross 2001) ...... 52
2.6. Dynamic LCIs of materials production stage, BM(i), between model years 1985 and 2020 ...... 56
2.7. Dynamic LCIs of manufacturing stage, BA(i), between model years 1985 and 2020...... 57
2.8. Dynamic LCIs of use stage, BU(i,j), for select model years based on driving 12,000 miles annually ...... 58
2.9. Dynamic LCIs of maintenance stage, BR(i,j), at vehicle age 1 based on driving 12,000 miles annually ...... 60
2.10. Dynamic LCIs of the end-of-life stage, BE(i,j), based on retiring vehicles in the tenth year ...... 61
3.1. A schematic example of the life cycle optimization (LCO) model based on four policies (B1, B2, B3, and B4 represent the final environmental burdens for the four policies.)...... 67
3.2. Cumulative environmental burdens accrued during the 36 years of time horizon when adopting energy/CO2-optimum and CO-optimum policies with 12,000 miles of annual VMT (The cumulative optimal environmental burdens are normalized to 1 for each criterion.)...... 73
viii 4.1. Survival rate curves for cars used in this study...... 82
4.2. Annual vehicle miles traveled (VMT) for cars developed for MOBILE6 ...... 83
4.3. Car fleet in 2000 based on MOBILE6 fleet characterization ...... 86
4.4. Average hazard rate profile (fraction per year) of car fleet based on MOBILE6 fleet characterization ...... 87
4.5. Relationships between new vehicle versus retired vehicle mileage for fleet optimization model ...... 88
4.6. Optimal fleet distributions and emission savings at 2001 for different pollutants...... 92
4.7. Vehicle age distributions for optimal fleet conversion between 2000 and 2020 (These are three separate programs, different for each pollutant.)...... 93
4.8. Emission reductions by the long-term optimal fleet conversion policy allowing 120 production ...... 95
4.9. CO2 cost and the optimal production (EPA 1992)...... 97
5.1. Basic fault-tree symbols (Haimes 1998; Raheja 1999)...... 107
5.2. Schematics of air/fuel (A/F) ratio control (Bosch 2000) ...... 108
5.3. Exhaust emissions based on air/fuel ratio (φ=1, if air/fuel ratio is 14.7:1.) (Stone 1999) ...... 109
5.4. Fault tree diagram for CO high-emitters...... 111
5.5. Fault tree diagram for NOx high-emitters ...... 112
5.6. Fault tree diagram for HC/CO high-emitters...... 114
5.7. Fault tree diagram for HC/CO/NOx high-emitters ...... 115
5.8. Breakdown of the IM147 test records based on 2000 I/M tests ...... 118
5.9. Results of the initial 2002 I/M tests for cars failed in the initial 2000 I/M tests ...... 120
5.10. Failure rates of cars tested during both 2000 and 2002 I/M cycles (See Tables 5.1 and 5.3 for details.)...... 121
ix 5.11. Emission profiles in the initial 2000 tests for the cars tested during both 2000 and 2002 I/M test cycles based on model years and high- emitter types (See Table 5.3.) ...... 127
5.12. Emission profiles of fail/pass cars across the 2000 and 2002 I/M cycles based on high-emitter types ...... 130
5.13. Part repair rates for CO high-emitters (3,091 cars) repaired during the 2000 I/M cycle...... 136
5.14. Failure rates in initial 2002 I/M tests for CO-type high-emitters ...... 137
5.15. Part repair rates for NOx high-emitters (6,982) repaired during the 2000 I/M cycle...... 138
5.16. Failure rates in initial 2002 I/M tests for NOx high-emitters ...... 139
5.17. Part repair rates for HC/CO high-emitters (3,120 cars) repaired during the 2000 I/M cycle ...... 140
5.18. Failure rates in initial 2002 I/M tests for HC/CO high-emitters...... 141
5.19. Repeat failure rates in the 2002 initial tests for HC/CO type fail/pass cars during the 2000 cycle based on repair parts (model year 1992- 1995) ...... 142
5.20. Part repair rates for HC/CO/NOx high-emitters (1,095 cars) repaired during the 2000 I/M cycle...... 145
5.21. Failure rates in initial 2002 I/M tests for HC/CO/NOx high-emitters...... 146
5.22. Failure rates and repair durability based on manufacturers...... 147
5.23. Emissions breakdown in the initial 2000 test based on high-emitter types...... 156
5.24. Breakdown of car fleets based on the initial 2000 and 2002 test results ....158
5.25. Emission factors for the calendar year 2000 measured by the IM147 cycles and those constructed based on the EPA’s in-use emission survey...... 163
5.26. Relationships between new cars’ mileage versus retired high-emitters’ mileage...... 165
5.27. Fleet distributions in 2000 and 2001 if all high-emitters are scrapped...... 167
x LIST OF TABLES
Table
2.1. Upstream environmental burdens for 1 kg of gasoline in 1995 (USAMP 1999)...... 27
2.2. Life cycle energy consumption for producing 1 kg of materials (MJ) ...... 36
2.3. Net shipments of steel mill products and primary aluminum production in the US...... 36
2.4. Total inputs of energy for heat, power, and electricity generation by iron and steel industries (SIC 331)...... 37
2.5. Total inputs of energy for heat, power, and electricity generation by primary aluminum production (SIC 3334) ...... 38
2.6. Energy conversion efficiencies of major energy types...... 39
2.7. Estimated primary energy consumption for steel industry (1012 Btu) ...... 39
2.8. Estimated primary energy consumption for primary aluminum industry (1012 Btu)...... 40
2.9. Estimated energy intensities and indexes for the steel and primary aluminum production (excludes some upstream processes)...... 40
2.10. Forecast of energy consumption per unit of output (1000 Btu/1992 dollar of output) in industrial sectors...... 43
2.11. Normal-emitter's FTP type emission factors (EN) with model year and mileage...... 50
2.12. Federal Certification Exhaust Emission Standards for light duty vehicles (g/mi) and estimated headroom values (%) for cars (EPA 2000; EPA 2000; Cullen 2001)...... 51
3.1. Optimal vehicle lifetimes and cumulative environmental burdens for a 36-year time horizon between 1985 and 2020...... 72
xi 4.1. Optimal new car production and emission savings at 2001 based on the survival rate scenarios (It is assumed that 100 new vehicles have been produced each year between 1981 and 2000.)...... 99
5.1. The numbers of cars that were tested in 2000 and 2002 initial tests ...... 119
5.2. IM147 cutpoints for gasoline cars (g/mi)...... 122
5.3. Breakdown of failed pollutants in the initial 2000 tests based on outcomes of initial 2002 tests ...... 124
5.4. Average emission profiles (standard deviations) in 2000 initial tests, 2000 retests, and 2002 initial tests based on the 2000 I/M results...... 126
5.5. Missing data rates in the VIRs for failing vehicles during the 2000 I/M cycle...... 133
5.6. Repair parts and costs for the fail/pass cars during the 2000 I/M cycle .....134
5.7. Emissions profiles of HC/CO high-emitters across the 2000 and 2002 I/M cycles based on repair parts ...... 143
5.8. Average emission factor profiles for cars tested during both the 2000 and 2002 I/M cycles...... 150
5.9. Fleet emissions (based on VMT profiles with age) in calendar year 2000 and 2002 for the cars tested during both 2000 and 2002 I/M cycle...... 151
5.10. Emission factors for all cars tested in the initial 2000 and 2002 tests...... 153
5.11. Fleet emissions in calendar year 2000 and 2002 for all cars tested in 2000 and 2002 initial tests (ton/year) ...... 154
5.12. HC/CO and NOx high-emitter contributions to fleet emissions...... 157
5.13. Fleet emissions (reductions) in 2002 based on I/M scenarios in 2000 (ton)...... 161
5.14. Life cycle emission reductions and additional car manufacturing requirements to offset VMT losses from scrappage scenarios ...... 168
xii LIST OF APPENDICES
APPENDIX
A. DYNAMIC LIFE CYCLE INVENTORIES ...... 180
B. SELECT DYNAMIC LCI PARAMETERS...... 205
C. FLEET OPTIMIZATION MODEL SOURCE CODE...... 209
D. BREAKDOWN OF HIGH-EMITTERS BASED ON MODEL YEARS...... 217
xiii ABSTRACT
SHAPING SUSTAINABLE VEHICLE FLEET CONVERSION POLICIES BASED ON LIFE CYCLE OPTIMIZATION AND RISK ANALYSIS
by
Hyung Chul Kim
Co-Chairs: Gregory A. Keoleian and Jonathan W. Bulkley
Although recent progress in vehicle technology and regulation has improved the environmental performance of new model vehicles, the continuing use of old, high- polluting vehicles contributes to air quality issues. While scrappage programs attempt to reduce emissions from old, high-emitting vehicles, life cycle assessment (LCA) studies show that scrapping old vehicles and manufacturing new vehicles also account for significant life cycle emissions. The expected median lifetime of automobiles increased from 12.5 years for model year 1980 to 16.9 years for model year 1990. But it is unclear whether this trend is optimal from an energy and environmental perspective. This study combines the LCA method with mathematical tools such as dynamic programming to determine optimal vehicle replacement/retirement policies. A life cycle optimization (LCO) model is developed and applied to mid-sized generic cars based on driving 12,000 miles annually, over a 36-year time horizon (between 1985 and 2020). For CO, NMHC, and NOx, automobile lifetimes ranging from 3 to 6 years are optimal for 1980s and early 1990s model years, while optimal lifetimes are 7 to 14 years for model year 2000s and beyond. On the other hand, a lifetime of 18 years minimizes cumulative life cycle energy use and CO2 emissions.
1 In addition, both ideal and practical fleet conversion policies were investigated from a life cycle perspective. According to the simulation results, accelerated scrapping policies increase greenhouse gases but reduce regulated emissions. These results are consistent with the results of the LCO model. Maintaining old vehicles in good condition would be another effective strategy for reducing emissions from high-emitting vehicles. The inspection and maintenance programs (I/M) have been used to identify and repair high-emitting vehicles. Based on Fault Tree Analysis (FTA) of emission control systems together with IM147 test records in the Arizona area, this study identifies limitations of specific repairs (e.g., catalytic converter repairs) for I/M improvement. Benefits of I/M programs and policy scenarios combining scrappage programs are also explored. The models used in this study can help policy-makers, manufacturers, and consumers understand the importance of issues regarding vehicle retirement decisions and guide decision-makers to environmentally sound fleet conversion strategies.
2 CHAPTER I
INTRODUCTION
Environmental issues associated with automotive transportation are diverse and encompass various time and spatial scales. Vehicle exhaust emissions such as carbon monoxide (CO), oxides of nitrogen (NOx), hydrocarbon (HC), and particulate matter (PM) contribute to local and regional air pollution. With a high vehicle density, major urban areas in the world are often exposed to smog and other air pollutants that cause human health and environmental issues (WBCSD 2001). One of the major concerns of smog is its ozone (O3) content. Ozone is formed from reactions between NOx and HC in the presence of sunlight, and causes eye irritations, respiratory discomfort, and reduced pulmonary function (Mondt 2000). In order to address these issues, federal certification standards have been regulating new model vehicles’ exhaust emissions since the 1960s. Currently, in the United States, the Tier 1 emission standards are in effect for cars and the Tier 2 standards are scheduled to phase in from 2004. These standards prompted major technological innovations for emission controls such as catalytic converters, exhaust gas re-circulation, and computer-based sensors and engine controls (Ross et al. 1995). In addition to these efforts for improving conventional vehicle systems, the California Air Resources Board (CARB) established a low-emission vehicle initiative program during the mid- and late-1990s by defining emission standards for transitional low-emission vehicles (TLEVs), low-emission vehicles (LEVs), ultra-low-emission vehicles (ULEVs), and zero-emission vehicles (ZEVs). Moreover, reliance on fossil fuels as a major source of transportation energy poses several environmental challenges. Automotive transportation is a major source of
1 greenhouse gases. In the United States, gasoline for automobiles alone accounts for 20%
of annual CO2 emissionsthe most dominant greenhouse gas. In addition, cars and light trucks account for a large fraction (38% in 2000) of US petroleum consumption (DOE 2002). The Energy Information Administration (EIA) predicts that world oil production will decline between 2021 and 2112, depending on assumptions regarding resource estimates and production growth rates (Wood and Long 2000). In the US, the predicted depletion of oil resources can pose a significant threat to energy and economic security due to the large share of imported foreign oil (54% in 2001) in overall petroleum consumption (EIA 2002). To improve energy efficiencies while maintaining comfort, safety, and driving performance, manufacturers have been exploring different propulsion systems and
fuelsconventional internal combustion engines (ICE), hydrogen fuel cells (FC), electric batteries, compressed natural gas (CNG), methanol, etc. One study estimates that, compared with a baseline fuel economy of 27.8 mile per gallon (mpg) in 1996, a hybrid FC vehicle is expected to run at 94.1 mpg while conventional ICE vehicles will be able to achieve 43.2 mpg in 2020 (Weiss et al. 2000). On the other hand, a recent European
study shows that well-to-wheel CO2 emissions and energy consumption of advanced ICE will be comparable to FC vehicles in the future, especially under cold start conditions (Ellinger et al. 2002).
Despite progress achieved through regulations and technology, air quality in many urban areas in the US is still far from satisfactory, and transportation energy consumption is increasing every year. These contrasts can be explained, in part, by increasing vehicle miles traveled and continued use of old, inefficient, high-emitting vehicles. In particular, a small number of high-emitting vehicles account for significant fractions of ozone precursors and toxic gases. The transition to a more sustainable transportation system requires a fleet conversion policy that efficiently absorbs new, clean technologies and retires old, high-polluting technologies.
2 Life extension strategies have been widely practiced as a resource management tool to reduce environmental burdens associated with product manufacturing. Designs that facilitate reuse, recycling, and remanufacturing have been applied to extend lifetimes of many product systems, including auto parts, manufacturing machines, and food containers (EPA 1993). On the other hand, accelerated scrapping of old, inefficient
carsa strategy directly contradictory to life-extension strategiesaims at reducing environmental burdens associated with product (vehicle) usage (U.S. Congress 1992; ECMT 1999). This study explores a new scheme for evaluating optimal automobile lifetime based on minimization of environmental burdens during the complete vehicle life cycle. Both the improving efficiency of new technologies and the deteriorating efficiency of old technologies are the driving forces for retirement/replacement decisions, while the environmental burdens from additional vehicle scrapping and manufacturing tend to offset the benefits of the retirement/replacement decisions. Another strategy to reduce the environmental burdens from the vehicle use stage is to maintain the emission performance of old vehicles through regular inspections and repairs. Although the Inspection and Maintenance (I/M) programs are used to identify and repair high-emitters, the effectiveness and benefits of these programs are not well understood at this point. In order to understand the vehicle deterioration and failure mechanisms, this study explores the paths to emission control system failures. Based on a real-world database together with theoretical fleet characterizations, this study investigates the benefits of maintaining and/or scrapping old vehicles.
1.1 High-Emitters
In the last few decades, automotive fuel consumption and exhaust emission per mile have decreased remarkably in the United States due to improvements in engine
3 technology, emission controls, and efficient vehicle design. This progress has been primarily driven by regulations including federal tailpipe standards and CAFE standards. These standards attempt to regulate mostly new vehicles based on laboratory tests such as the Federal Test Procedure (FTP). In spite of this recent progress, vehicle emissions1 are still significant in most urban areas. The causes behind this discrepancy include malfunctions of emission controls, and increasing vehicle miles traveled (VMT), and the fact that actual driving conditions differ from laboratory test conditions. In the real world, vehicles are often driven at higher power than the federal test conditions, and therefore emit much more than during the tests. Moreover, emission control systems of cars typically deteriorate and sometimes fail with increasing vehicle mileage or age. The exhaust emissions from properly maintained and driven cars generally increase gradually. The emission systems of such cars, called normal-emitters, deteriorate in proportion to mileage (Austin and Ross 2001). On the other hand, emissions from poorly-maintained and poorly-driven cars often jump abruptly. The emissions per mile from these failed cars, called high-emitters, are considerably higher than the certification standards. The probability that a car is a high-emitter also increases with increased mileage. A number of independent analyses indicate that a small fraction of the high- emitters account for a significant proportion of real-world automotive emissions. An early study sponsored by the California Air Resources Board (CARB) showed that on- road hot-exhaust emissions were predominated by 10-20% of gross polluters. In particular, the highest emitting 3% of the vehicles contributed 23% and 27% of CO and HC based on a roadside survey study in California (CARB 1994). The significance of high-emitters can be evidenced in real-world emissions, which include on-cycle exhaust, off-cycle exhaust, evaporative, malfunction, and upstream emissions. One study shows
1 In this study, the term “emissions” does not include CO2 or H2O emissions unless specified.
4 that the malfunction of emission control systems is responsible for 6 g/mi, 0.6 g/mi, and 0.8 g/mi of CO, HC, and NOx over 17 g/mi, 1.7 g/mi, and 1.8 g/mi of lifetime average real-world emissions for a model year 1993 car (Ross et al. 1995). Note that the 1993 tailpipe emission standard was 3.4 g/mi, 0.41 g/mi, and 1.0 g/mi for CO, HC, and NOx respectively. Remote sensing studies also support the significance of high-emitters. Between 1998 and 2000, three rounds of on-road remote sensing measurements in the Phoenix area concluded that the dirtiest 10% of the entire fleet accounts for 71-78%, 66- 79%, and 49-56% of the CO, HC, and NO, respectively (Bishop et al. 1999; Pokharel et al. 2001; Pokharel et al. 2002).
1.2 Scrappage Programs
Both to reduce the emissions from high-emitters and to reduce excessive gasoline consumption by inefficient vehicles, many countries have attempted to scrap old vehicles before their economic lifetimes have been exhausted. Scrappage programs recruit and scrap high-polluting vehicles in exchange for some compensation to the vehicle owners. In a ‘cash-for-scrappage’ program, vehicle owners receive awards simply by scrapping high-emitters. On the other hand, a ‘cash-for-replacement’ program requires replacement of old cars with new models in order to receive compensatory awards. Often, these awards can be traded between vehicle owners, brokers and/or industries in the form of emission credits (EDF and GM 1998; ECMT 1999). Scrappage programs also accelerate absorption of new emission technologies. By eliminating high-emitters, scrappage programs create new car demand. As a result, scrappage programs support car-manufacturing industries and may contribute to national economies (Bohn 1992; ECMT 1999). The introduction of more reliable and safer vehicles may enhance transportation safety as well (U.S. Congress 1992; ECMT 1999).
5 Examination of changes in accident statistics as a result of scrappage programs, however, has not been undertaken and is not attempted in this study. A number of European countries implemented scrappage programs based on different incentive schemes. In February 1994, France introduced the first scrappage program awarding Fr 5,000 (approximately $950) to individuals replacing cars older than ten years with new models. France ran a second program between October 1995 and September 1996, this time with variable incentives based on the size of the new car. These two programs are estimated to have retired 860,000 vehicles that would have not been scrapped as soon without such incentives. In 1994 and 1995, Spain temporarily introduced a scrappage program that awarded tax benefits ranging between Pta 85,000 and 100,000 ($630-750) for replacing a car older than ten years with a new car. The scrappage program boosted new car demands in Spain and this tax incentive program became permanent in 1997. In addition to these countries, Greece, Hungary, Denmark, Ireland, Norway, and Italy also introduced scrappage programs either temporarily or permanently. These programs aimed to eliminate two-stroke-engine cars, cars without catalytic converters, old buses, and old trucks. The incentive ranged between $500 and $1600 depending on the characteristics of both retired and new vehicles (ECMT 1999). In the US, as a result of the Clean Air Act Amendments (CAAA) of 1990 that included market-based emission reductions, scrappage programs have emerged as an attractive tool to reduce mobile source emissions. Such programs were also intended to provide stationary sources with more flexibility through emission trading with mobile sources. The Union Oil Company of California (UNOCAL), in cooperation with the California Air Resources Board (CARB) and the California Department of Motor Vehicles, first introduced a scrappage program on June 1990 in the Los Angeles Basin area, offering $700 for retiring eligible vehicles. The program was designed to eliminate pre-1971 models. As a result, over 8,000 vehicles, accounting for more than 2% of the targeted model years in this area, were retired. Later, smaller scale scrappage programs
6 were implemented in Phoenix, Chicago, and some cities in California, offering $500 to $600 per scrapped car. Unlike most European programs, ‘cash-for-scrappage,’ which does not require one to buy a new car, dominates in the US. In the US, most scrappage programs have been limited to pilot programs and have been financed by private companies seeking emission credits to meet compliance requirements on stationary sources (U.S. Congress 1992; Alberini et al. 1995; EDF and GM 1998; ECMT 1999). Currently, debates among automakers, lawmakers, and other stakeholders discuss whether such policies actually improve environmental quality or simply weaken the part supply infrastructures for restoring vehicles (Stoffer 2002). Scrappage programs suffer from several limitations. First, the environmental benefits of the scrappage programs are uncertain. Emission benefits from scrappage programs can be determined by the emission factors of the high-emitters, the avoided travels of high-emitters, and vehicle owners’ travel patterns after scrapping the high- emitters. Other than the difficulties in measuring real-world emissions from high- emitters, it is problematic to accurately estimate the avoided vehicle miles traveled (VMT) of the scrapped vehicles. Moreover, it is uncertain how the avoided VMT of the scrapped vehicles is made up by newer vehicles or existing vehicles, especially in ‘cash- for-scrappage’ programs that do not require the purchase of a new vehicle (U.S. Congress 1992). Several studies indicate that the emissions reduction is questionable without large bounties (Alberini et al. 1995; Hahn 1995). For example, an economic analysis of the scrappage program using Los Angeles as a model area estimated that HC and NOx reductions would be under 1% in the case of a $250 bounty while 2%-6% reductions are expected in the case of a $1,000 bounty (Hahn 1995). Second, such programs will bring about a shortage of old cars, which may eventually increase the price of older vehicles and may harm mobility for low-income individuals. This may also cause the migration of old vehicles from non-scrappage program areas to scrappage program areas, and the emission benefits from the program areas will be significantly reduced. Finally,
7 scrappage programs are exposed to possible fraud. For example, if the bounties for old cars are determined by emission tests, the vehicle owners or brokers can tamper with vehicles to inflate emissions in order to receive more credits (EDF and GM 1998). Several policy analysts explored a hybrid option combining a scrappage program and an inspection and maintenance (I/M) program that is described in the next section. This I/M option states that vehicles failing I/M emission tests and exceeding certain amount of repair costs should be scrapped with appropriate rewards. By selectively scrapping high-emitting vehicles, such options are likely to improve the cost/benefit ratios of accelerated vehicle retirement programs (U.S. Congress 1992; Hahn 1995; Alberini et al. 1998).
1.3 Inspection and Maintenance (I/M) Programs
An inspection and maintenance (I/M) program requires a vehicle to pass a certain emissions test before a new registration or a renewal of the registration. I/M programs have been widely implemented in the US compared to scrappage programs. Currently, in the US, more than 30 states conduct I/M programs with a variety of test processes, fees, and frequencies. The tests are conducted either annually or biennially; statewide or within specific jurisdictions; and with or without costs. States adopt various test methods depending on the programs. In 1981, the Environmental Protection Agency (EPA) first mandated inspection and maintenance requirements to register vehicles in non-attainment areas. The initial tests consisted of idle tests and 2500 rpm tests. However, these programs were questionable in identifying high-emitting vehicles because such simple tests were incapable of simulating real-world driving conditions (Mondt 2000). In 1995, the IM240
8 test cycle developed by the EPA was introduced to “enhanced” I/M program areas2 including Phoenix, AZ and Denver, CO. The IM240 tests are conducted on a dynamometer with measurement tools for HC, CO, and NOx. The IM240 cycle shares large parts with the Federal Test Procedure (FTP) in its acceleration schedule and maximum speed (Mondt 2000). Although the test can last up to 240 seconds, decisions are often made based on a shorter time period, depending on the emission performance of the vehicles tested. Many states, including California, Connecticut, Utah, and Washington, use alternative measuring cycles called Accelerated Simulation Mode (ASM) in the “enhanced” I/M areas due to the low costs for the test equipment compared with IM240. The ASM is a steady-state loaded mode test under either 50% or 25% of the maximum acceleration in the FTP (CARB 2000). Some states, like Oregon and Massachusetts, use the BAR31 cycle (developed by the California Bureau of Automotive Repair) as a surrogate to IM240, primarily because of the reduced test time and ease of interpretation. The BAR31 is a 31-second test cycle similar to IM240 in its early acceleration. It runs, however, at a maximum speed of only 48 kilometers/hour. The identical cycle may be repeated up to four times, depending on emission performance, to assure correct interpretations (Greater Vancouver Regional District 1998). Most recently, the state of Arizona has begun to use the IM147 cycle to replace IM240. The IM147, which is equivalent to the last 147 seconds of IM240, aims at reducing the test time without harming the accuracy of IM240. To prevent false results, the IM147 is designed to be repeated up to three times (EPA 1999). A new approach is now being adopted: new cars and trucks are equipped with OBD (on-board diagnostic) systems to identify emission control problems. Many states are beginning to add OBD system checks to the I/M requirements for the 1996 and later models (EPA 1999).
2 Under the Clean Air Act Amendments (CAAA) 1990, EPA classifies I/M programs either as “basic” or “enhanced,” depending on the local air quality.
9 European countries including Germany and Austria also adopted vehicle inspection programs in the early 1990s, based primarily on a no-load short test called ‘idle/fast-idle test3.’ Especially for two-way catalyst vehicles, the ‘lambda tests’ were added to the existing ‘idle/fast-idle test’ to examine the air/fuel ratio, and became mandatory in all EU countries under Directive 92/55/EC beginning in 1997 (European Commission 1998). Although a number of evaluations of I/M programs have been conducted based on different methods, the success of I/M programs is uncertain at this point. Leaving aside the fleet turnover rates in the program area, many uncertainties associated with tests make it difficult to estimate the effectiveness of the I/M programs. First, in the real world, vehicles are driven under heavier loads than during tests. Second, vehicle owners often repair emission control systems before I/M tests to avoid the inconvenience of an I/M test failure. Finally, I/M emission tests suffer from inherent uncertainties due to the random fluctuations of vehicle emissions (Wenzel 2001; Wenzel 2001). The EPA originally projected that, by 2000, 28% of volatile organic compounds (VOCs), 31% of carbon monoxide (CO), and 9% of oxides of nitrogen (NOx) would be reduced in average fleet emissions for each test cycle (EPA 1992). However, the emission test records of I/M programs seem inconsistent with the EPA’s projections, especially for HC and CO. Investigating the emissions of initial tests, after-repair tests, and final-pass tests, Wenzel estimated the reductions from the 1997 IM240 program for Phoenix, AZ to be 14%, 17%, and 7% for HC, CO and NOx respectively (Wenzel 2001). Other analyses of the database of the Arizona I/M program show consistent reduction ranges of 12-16% for HC; 15-18% for CO; and 7-8% for NOx (Glover and Brzezinski 1997; Wenzel 1999; Ando et al. 2000; Wenzel 2001). Sierra Research also estimated 9%, 13%, and 4% of HC, CO and NOx emissions from the Canadian Vancouver/British Columbia ASM
3 In ths US, an ‘idle/fast-idle test’ is called a ‘two-speed idle (TSI) test.’
10 program based on before and after repair tests (Sierra Research 1998). However, these estimations do not account for missing vehicleswhether scrapped, sold or registered in other states, with owners avoiding I/M requirements, etc.which are known to be 22- 33% of the initially-tested vehicles (Glover and Brzezinski 1997; Ando et al. 2000; Wenzel 2001). If the no-final-pass (missing) vehicles are either scrapped or sold to non- program areas after failing the tests, the emission reductions of I/M program are underestimated. Conversely, the program benefits will be harmed if the no-final-pass vehicles are driven continually in the program area, possibly using fraudulent or illegal methods. Moreover, the emission reductions achieved by I/M programs significantly disappear between I/M cycles. Based on the analysis of the Phoenix IM240 program, 40% of the vehicles that had failed the initial 1995 I/M tests and eventually passed the tests later in 1995, again failed the initial 1997 I/M tests. Furthermore, half of the repeated failures across the two I/M tests suffer from an identical set of excessive emissions. This suggests that the durability of I/M repairs may be questionable. In other words, many repairs are simply temporary measures to pass the emission tests. More importantly, although a majority of the vehicles (more than 80%) pass their initial tests, the normal deterioration of these initially-passed vehicles may diminish most of the benefits gained in an I/M program cycle (Wenzel 2001). Considering the difficulties discussed above, remote sensing can provide more accurate information on the effectiveness of the I/M programs, since it measures tailpipe emissions in real driving conditions. It can also detect those vehicles driven in the program areas but avoiding I/M tests. Overall, remote sensing studies show that the I/M benefits are smaller than those calculated from the test records. Based on remote sensing studies of the Denver area, the CO reduction was estimated to be 4%±2% for each I/M cycle since 1995, compared with the 20-23% of reduction claimed by the State of Colorado. Moreover, no reduction has been observed for HC and NOx emissions
11 (Stedman et al. 1997; Stedman et al. 1998). A remote sensing study of the Phoenix area also indicated smaller benefits of HC and CO emissions, 11.5% and 6.9% respectively, than the calculated reductions from the I/M records (Wenzel 2001). Remote sensing is often criticized because it measures only a single second of an emission profile while driving conditions change every second in real driving. Inherent errors associated with remote sensing measurements are inevitable as well. To address these issues, multiple measurements are often conducted for a vehicle and a large number of samples are recommended. A minimum of 20,000 valid readings is required for the most basic remote sensing device (RSD) analysis. Furthermore, adequate coverage to obtain measurements representative of the fleet is also a pragmatic challenge.
1.4 Problem Statements
As described above, the efforts to reduce mobile emissions through fleet management policies such as scrappage programs and I/M programs pose significant limitations in their implementation and evaluation. This study identifies several key issues of the scrappage and I/M programs and develops models to address these issues. First, most studies on scrappage programs focus on the emissions of vehicle operations and fail to consider other sources of emissions: dismantling old vehicles and producing new vehicles. A recent study by the European Conference of Ministers of Transport (ECMT) concluded that scrappage schemes have both positive and negative impacts on the environment. The positive impacts include the reduction of atmospheric emissions when new vehicles replace old vehicles. The negative impacts, on the other hand, arise from the increased emissions and resource consumption from the additional vehicle scrapping and production processes (ECMT 1999). Wee et al. also argued that scrapping old cars would result in a net increase of life cycle energy use and CO2 emissions unless fuel economies improve considerably for the cars manufactured in the
12 future (Wee et al. 2000). Thus, the benefits or emission credits created through scrappage programs might be exaggerated since the negative impacts of vehicle production and disposal were ignored. Moreover, the negative impacts associated with vehicle production and disposal may be transferred to other areas with vehicle manufacturing and disposal facilities. Second, current studies on the scrappage programs fail to provide a normative framework for a scrappage decision in terms of environmental criteria. Thus, for example, questions such as how many vehicles should be scrapped from a fleet; whether a scrappage program is necessary if emission control systems become more durable; or whether more scrappage programs are needed if cleaner and efficient alternative fuel vehicles are available in the future, remain unanswered. Optimal scrappage decisions may depend on the relative emission performances between the retiring and new vehicle technologies rather than the classification of high- or low-emitters based on emission measurements. In fact, vehicle replacement decisions have been widely studied in the context of economic benefit optimization based on dynamic models. Several studies investigated optimal business strategies regarding purchasing and selling fleets of buses, trucks, and cars. Optimal decisions take into account salvage values, tax laws, fuel and maintenance costs, depreciation, and the price of new models. Researchers use dynamic programming or linear programming tools to calculate optimal decisions for maximizing cash flow up to certain future time (Waddell 1983; Simms et al. 1984; Bean et al. 1994). Extending such tools to environmental considerations allows vehicle owners, policy makers, and manufacturers to understand the underlying environmental impacts of the current retirement practices. In the real world, vehicle retirement is decided by economic considerations, including repair cost, market price, and scrap price of a used vehicle; and technical considerations, including engine size, vehicle age, and manufacturers (Manski and Goldin 1983; Berkovec 1985). The expected median lifetime of an average car has increased from 12.5 years for model year 1980 to 16.9 years for
13 model year 1990 (DOE 2002). However, the environmental impacts of such trends are not yet well understood. Finally, although I/M programs have been successful in identifying high-emitters through comprehensive test cycles, it is still unclear whether the identified high-emitters are repaired properly. A significant fraction of the vehicles that fail an I/M test repeats the identical types of emissions failures in the next cycle of I/M tests (Wenzel 2001; Wenzel 2001). Moreover, few investigations have been conducted on the causes and types of emission failures, especially for repeating high-emitters. Although studies have investigated the frequencies of specific component failures, a systematic approach to relating a component failure with other component failures has not been attempted (Heirigs and Austin 1996; Harrington et al. 2000; Eastern Research Group 2002). Such an approach, if possible, would help increase the benefits of I/M program since the benefits diminish due to repeated component failures or deterioration after alleged successful repairs.
1.5 Dissertation Outline
To consider complete life cycles of vehicles, this study uses a Life Cycle Assessment (LCA) tool, which is based on emissions, energy use, and resource consumption from the entire vehicle life cycle: materials production, component manufacturing and vehicle assembly, and maintenance and disposition. Among the environmental measurements, non-methane hydrocarbons (NMHC), carbon monoxide
(CO), oxides of nitrogen (NOx), carbon dioxide (CO2) and energy consumption are the objectives to be considered for this study. NMHC, CO and NOx are regulated auto emissions and the significance of these pollutants depends on the air quality of each location. On the other hand, CO2 and energy consumption are global issues influenced by CAFE standards in the US.
14 Although there has been extensive research on the environmental impacts of product life cycles, most studies have been performed based on a quantified performance of a product system called ‘functional unit’ (ISO 1997). In order to determine the optimal vehicle retirement age to minimize environmental impacts, the LCA measurements need to be conducted on a vehicle age and model year basis. For this purpose, a new method to estimate life cycle emissions of different model year cars has been developed. The estimated inputs and outputs (emissions and energy consumption) associated with different model year cars and ages are modeled by the “Dynamic Life Cycle Inventories (LCIs),” and Chapter 2 presents the procedures for the method. Dynamic LCIs are estimated for mid-sized generic cars with model years between 1985 and 2020 and for the ages of up to 20 years. Dynamic LCIs are indexed by the variables of model year and age and are used as inputs for exploring the optimal vehicle lifetimes and fleet scrappage policies in Chapters 3 and 4. In Chapter 3, optimal vehicle lifetimes are investigated in terms of environmental objectives by applying an engineering optimization tool called ‘dynamic programming’ to the Dynamic LCIs of mid-sized internal combustion engine vehicles. Sets of optimal lifetimes for each pollutant as well as energy criterion are determined and compared with each other. The determinants for optimal vehicle life are discussed based on the Dynamic LCI trends. A multi-objective analysis is undertaken to identify a globally optimal scrap policy. In addition, policy recommendations for improving vehicle energy and emission policies are provided for consumers, manufacturers, and policy makers based on the simulation results. Chapter 4 presents a fleet-based model to optimize scrapping of older cars or a "fleet conversion policy" for internal combustion engine vehicles in the US. The optimal policy minimizes each regulated emission (CO, NOx, and NMHC) as well as greenhouse gas emission (CO2) from a fleet of vehicles, given a particular mileage total for the fleet. The Dynamic LCIs developed in Chapter 2 describe the emission profiles for each model
15 year and age. The results of the simulation provide the threshold ages for vehicle scrapping and the number of new vehicles to replace the scrapped vehicles. It is assumed that only the age (mileage) of the vehicle, not the condition of its emissions control, is involved in the decision to scrap. In Chapter 5, a Fault Tree Analysis (FTA) is performed to illustrate the path to the different types of high-emitters such as CO, HC, and NOx high-emitters or any combined types of the high-emitters. The FTA is a risk analysis tool used to trace the root causes of failures by deductive processes. Appropriate repair strategies are discussed for each high-emitter type based on the FTA diagram. Then, the Arizona IM147 test and repair records are analyzed as a way to verify the FTA. The durability of the repairs is assessed by tracing the failed and repaired vehicles over the 2000 and 2002 I/M cycles4. The effectiveness and benefits of the I/M program are discussed based on current I/M programs and possible I/M repair strategies. Finally, a hybrid strategy, combining I/M and a scrappage program, is explored. Chapter 6 concludes this study and provides implications of the results. The Appendix includes the Dynamic LCI results, select parameters, and the computer codes for the model in Chapter 4.
4 The Arizona I/M program requires a biennial vehicle test.
16 CHAPTER II
DYNAMIC LIFE CYCLE INVENTORY
2.1 Vehicle LCA
The International Organization for Standardization (ISO) defines Life Cycle Assessment (LCA) as "compilation and evaluation of the inputs, outputs and the potential impacts of a product system throughout its life cycle"(ISO 1997). LCA provides the environmental profile of a system in which burdens and impacts are distributed across the life cycle. Since LCA uses a systematic and comprehensive approach to assess environmental burdens associated with products, it has been used as an analytic tool for pollution prevention, life cycle design and life cycle management (Keoleian 1995; Keoleian 1995; Keoleian et al. 1997). The complete LCA framework includes four phases: goal and scope definition; inventory analysis; impact assessment; and interpretation (ISO 1997). The goal and scope definition defines the purpose, audience, and system boundaries. The inventory analysis involves data collection and calculations to quantify material and energy inputs and outputs of a product system, and the impact assessment evaluates the significance of potential environmental impacts based on the inventory analysis. Finally, the interpretation evaluates findings, reaches conclusions and makes recommendations. A vehicle life cycle consists of the following generic stages: materials production, manufacturing and assembly (hereafter referred to as manufacturing), use, maintenance and repair (hereafter referred to as maintenance), and end-of-life. The life cycle profile shows that the distribution of burdens across each life cycle stage may differ depending
17 on the type of environmental burden. According to the US Automotive Materials Partnership (USAMP) study which measured the life cycle inventories of a generic 1995 mid-sized car, the use phase contributes 85% of the total life cycle energy consumption (see Figure 2.1) based on 120,000 miles of service life (USAMP 1999). On the other hand, the use phase contributes only 19% of the total solid waste produced while the materials production phase contributes 58% of the total solid waste produced. Most LCA studies on vehicle systems were conducted in the 1990s. Automotive parts and components were the topics of the early stage investigations in vehicle LCA. The topics included engine oil filters, aluminum intake manifolds, instrument panels, and fuel tank systems, and the LCA studies provided environmental criteria for the Life Cycle Design of vehicle components (Keoleian 1995; Kar and Keoleian 1996; Keoleian et al. 1997; McDaniel 1997; Keoleian et al. 1998; Stephens et al. 1998; Shen et al. 1999). Vehicle LCA then moved to investigating total vehicle systems with a variety of powertrains, such as Internal Combustion Engine Vehicles (ICV), Electric Vehicles (EV), Compressed Natural Gas Vehicles (CNGV), and Fuel Cell Vehicles (FCV). A study by Schuckert et al. compared the life cycle energy and emissions of a small car (650 kg) and an automobile of upper standard (2000 kg). The study revealed that the upper standard consumes nearly 900 GJ of life cycle energy compared with 312 GJ for small car based on the 200,000 passenger kilometers as a functional unit (Schuckert et al. 1995). Sullivan and Hu also introduced a life cycle energy model for ICV and EV. The study showed that the EV model investigated consumes 24% less life cycle energy than a gasoline ICV, although the ICV has many advantages in costs, accelerations, refueling times, and driving ranges (Sullivan and Hu 1995). LCA studies also explored future transportation strategies. The Energy Laboratory at MIT conducted an LCA study of new and future automobile technologies. The study estimated life cycle energy use, greenhouse gas emission, and costs associated with various technologies in 2020 including ICV, FCV, EV and hybrid vehicle systems.
18 In the report, the hybrid hydrogen FCV and hybrid gasoline FCV consumes 72% and 104% of life cycle energy compared with the baseline gasoline ICV for 2020 (Weiss et al.
2000). Most recently, Ellinger et al. compared life cycle CO2 emissions and energy consumptions of various powertrains of the future. The study showed that the optimized diesel drive train is the most energy efficient and the CNGV vehicles produces the lowest
CO2 emissions. FCVs are as efficient as the diesel and CNGV vehicles only under hot start conditions (Ellinger et al. 2002). The most comprehensive LCA study of a total vehicle was the 1995 USAMP project. A generic vehicle was conceptualized as a synthesis of three 1995 comparable mid-sized vehicles: the Dodge Intrepid, the Chevrolet Lumina and the Ford Taurus. Life cycle inventory models were developed for each detail of the materials and components of the generic vehicle, primarily based on North American manufacturing and vehicle operation (Sullivan et al. 1999; USAMP 1999).
2.2 Dynamic LCI Conceptual Structure
Vehicle LCA studies have mostly focused on measuring the environmental performance of specific model year vehicles or propulsion systems. However, such methods fall short of describing high or low emitters since these studies report average environmental performances based on functional units (e.g., 120,000 miles of driving). In order to compare the environmental performance between old, retiring vehicles and new replacement vehicles in the context of scrappage programs, LCA models need to be developed for each model year as a function of vehicle age. In particular, this section presents a dynamic LCI method for the generic vehicle defined in the USAMP project. The USAMP generic vehicle is different from an average mid-sized vehicle since it is a hypothetical synthesis of three US mid-sized car models.
19 1000
800
600
400 Energy (GJ)
200
0 r ion bly se ai ife ct m U ep f-L du se R -o ro As & nd P & ce E t'ls g. an a Mf en M int Ma Life Cycle Stage
Figure 2.1: Life cycle energy consumption of a generic mid-sized passenger vehicle based on 120,000 miles of driving (USAMP 1999)
The dynamics of environmental performance based on a particular model year are described in Figure 2.2. The environmental performance of the life cycle at the bottom of Figure 2.2 changes as the three dynamic LCI factors at the top of Figure 2.2 change with each model year. The regulatory/socioeconomic factors describe demographic, regulatory, and macro-economic changes. The technology improvements affect emission controls, powertrain, and other vehicle designs for energy efficiencies. The deterioration of a vehicle is often determined by driving conditions and maintenance. For most of the model years, changes in one dynamic LCI factor influence other factors. For example, technology improvements may enhance the durability of emission control systems such as
20
Regulatory/ Socio- Technology Factors Deterioration economic Improvement Behavior Factors
Parameters Recycled Materials VMT Energy Fuel Emission Component Content Use Intensity Economy Factor Reliability 21
Life Cycle Stage Materials End-of-Life Production Manufacturing Use Maintenance
Figure 2.2: Factors, parameters, and life cycle stages illustrating the dynamic LCI for a generic vehicle
catalytic converters and, accordingly, change vehicle deterioration behavior. The LCI parameters in Figure 2.2 are defined to reflect the evolution of these dynamic LCI factors. These LCI parameters often change by evolutions of multiple factors. Finally, parametric changes for a model year (i) and vehicle age (j) affect the life cycle environmental burdens (BM(i), BA(i), BU(i,j), BR(i,j), and BE(i,j)). For example, BU(i,j) is the environmental burden for the use phase where i and j represent, respectively, model year and age.
2.3 Dynamic LCI Modeling for a Generic Vehicle
The dynamic LCIs in this study account for the five life cycle phases: materials production, manufacturing, use, maintenance, and end-of-life. For each phase, energy
consumption, CO2, CO, NOx, and NMHC emissions have been obtained as functions of vehicle age and model year. CO, NMHC, and NOx are regulated auto emissions that can
have a significant impact on local and regional air quality. On the other hand, CO2 and energy consumption are global issues influenced by CAFE standards in the US. Other environmental burdens, such as particulate matter (PM), methane, and air toxics are not considered, primarily due to data limitations. For instance, since the current federal emission standards do not regulate PM for cars, estimations regarding the PM profiles of past and future model year cars are highly uncertain. The dynamic LCIs in this study build on the LCI analysis of a generic vehicle conducted by the US Automotive Materials Partnership (USAMP). The analysis measured the raw materials use, emission and waste outflows, and energy use from the life cycle of a 1995 mid-sized generic vehicle, a hypothetical synthesis of the Dodge Intrepid, the Chevrolet Lumina, and the Ford Taurus (USAMP 1999). Except for the use phase, the dynamic LCIs for this study have been determined based on the LCI of a 1995 generic vehicle and the LCI parameters of each model year and vehicle age. Thus, the
22
dynamic LCIs for model year i and age j, B(i,j) can be derived from the USAMP LCI
(BU) using parameter P(i,j):
∏ Pijk (), kI∈ U B()ij, =×∗ B ∏ Pk kI∈ where P* denotes the baseline parameter value applied to the 1995 generic vehicle. Note that k and i represent, respectively, the index for the parameter and the set of parameters defined for each life cycle stage. The detailed form of the parameter in the above equation varies according to the life cycle stage. For example, while total vehicle weight represents the LCI parameter for the manufacturing stage, detailed material composition data are used for characterizing the materials production stage. In addition, surrogate parameters are developed for modeling purposes. For instance, environmental burdens to produce a unit mass of materials are characterized based on the recycling scenario for each model year. These burdens represent the recycled content parameter in Figure 2.2. The dynamic LCIs for the use phase have been modeled separately from the USAMP study. The detailed modeling processes of dynamic LCIs for generic US mid-sized passenger cars with internal combustion engines between model years 1985 and 20205 are described in the following sections.
2.3.1 Materials Production
The materials production phase, which makes the second largest contribution to the total environmental burdens of vehicle life cycle, is reported to account for 9% of energy consumption and 8% of CO2 emission over the life cycle of a generic vehicle
5 While Chapter 3 of this study uses the dynamic LCIs for the model years between 1985 and 2020, model runs in Chapter 4 are based on the dynamic LCIs for model years between 1981 and 2020. Dynamic LCI models in this chapter are readily expandable up to model year 1981, although the uncertainties associated with emission factors of early 1980s model years are unavoidable.
23
(USAMP 1999). This phase of life cycle includes raw material acquisition, materials transportation, and material processing prior to the component manufacturing and assembling of vehicles. As shown in Figure 2.2, the materials use and energy intensity6 determine the materials production life cycle inventories. The materials use parameter specifically refers to vehicle materials composition. The following notations and equations determine the materials production environmental burdens for each model year and age for the generic vehicle.
m(a,i) : Mass of material a for model year i vehicle
EI(a,i) : Energy intensity for producing material a for model year i vehicle.
R(a,i) : Composite LCI for producing a unit mass of material a based on the recycled content of material a in calendar year i.
i* : Baseline year (=1995).
U BM (a) : USAMP environmental burden for producing material a .
For each environmental category (CO, HC, NOx, energy consumption, etc.), the environmental burdens to produce material a for the generic vehicle of model year i,
BM(a,i) is determined as follows.
mai(,) EIM (,) ai Rai (,) U BM (,)ai=×∗∗∗ ×× BM () a mai(, ) EIM (, ai ) Rai (, )
For each environmental category, the total environmental burden to produce complete vehicle materials at each model year i is obtained by aggregating burdens from all of the materials.
6 This can be called “emission intensity” if used for estimating dynamic life cycle emissions. However, due to difficulties in data collection, energy intensities are used as surrogates in most cases. This assumption can be justified by the fact that fossil fuels account for major fraction (~85%) of primary energy consumption in the US, and most air emissions are associated with combustion of fossil fuels (EIA 2002).
24
BM (i) = ∑ BM (a,i) a
2.3.2 Manufacturing
The manufacturing phase is classified into three types of activities: parts fabrication, component manufacturing and vehicle assembly. Parts fabrication represents the primary transformation of engineered materials including stamping, casting, molding, and forging. Discrete parts often require many other processing steps, such as machining, surface treatment, welding, fastening, etc. Component manufacturing involves further processing and assembly of parts into higher-level components. Final vehicle assembly integrates all parts, components, and fluids together into the final vehicle. As shown in Figure 2.2, manufacturing phase dynamic LCIs are associated with two parameters: materials use and energy intensity. Unlike the materials production phase, the materials use parameter here specifically refers to a total vehicle weight. The environmental burden of this phase is assumed to be linearly dependent on the total weight of the vehicle. The manufacturing phase dynamic LCIs are then estimated as functions of the model years. As noted in Figure 2.2, vehicle total weights are influenced by the regulatory/socioeconomic factors and technology improvements. CAFE regulations and development of light materials have driven weight savings in the past. Consumer awareness of environmental issues is another factor (although currently weak) in weight savings (OSAT 2000). On the other hand, crash requirements, manufacturing costs, and consumer preferences for optional vehicle functions can result in additional vehicle weight. Table B1 in Appendix presents the total weights of average vehicles used for this model.
25
U BA : Environmental burden for the manufacturing phase in the USAMP.
mT () i : Average total weight of a model year i vehicle.
EIA (i) : Energy Intensity of manufacturing for model year i . i* : Baseline year (=1995).
Then the environmental burden for manufacturing at year t, BA(i) is written as follows.
mi() EI() i T A U BA ()iB=×∗ ×A mi() EI i* T A ()
2.3.3 Use
The environmental burdens associated with vehicle use can be classified as precombustion and combustion burdens. Precombustion environmental burdens are often called ‘upstream’ burdens since they consider crude oil acquisition, transportation, oil refining, and other intermediate processes required to produce and deliver fuels. Table 2.1 gives the precombustion environmental burden to produce 1 kg of gasoline used in the USAMP study (USAMP 1999). Combustion environmental emissions for the vehicle use phase are associated with vehicle tailpipe emissions7 while combustion energy consumption refers to the heating value of a fuel. The heating value8 of 1 kg of gasoline fuel is 47 MJ.
Table 2.1: Upstream environmental burdens for 1 kg of gasoline in 1995 (USAMP 1999)
Energy(MJ) CO2(g) CO(g) HC(g) NOx(g) 10.7 702.6 0.4 3.9 1.7
7 For modeling purposes, evaporative HC emissions are included in the combustion emission. 8 Higher heating value. This consists of net heat content (lower heating value) and the energy spent to generate water vapors in the combustion gas.
26
Figure 2.3 shows the detail dependencies between the parameters and environmental burdens. In this study, the precombustion and the combustion environmental burdens are functions of VMT and fuel economy parameters. In addition, the precombustion environmental burden depends on the energy intensity for producing gasoline, and the combustion environmental burden depends on vehicle emission factors.
Energy Fuel Emission VMT Intensity Economy Factor
Pre- Manufacturing Combustion Maintenance combustion
Figure 2.3: The detailed dependencies between the parameters and environmental burdens of the use phase life cycle
The following notations and equations determine dynamic LCIs for the vehicle use phase.
P BU () i,j : Use phase precombustion environmental burden for model year i and age j (=1,2,3… ).
C BU () i,j : Use phase combustion environmental burden for model year i and age j. VMT() i,j : Vehicle miles traveled per year.
27
EIg () i,j : Energy intensity for gasoline production in calendar year i+j-1.
U Bg : Precombustion environmental burden to produce 1 kg of gasoline in the USAMP study.
FE() i,j : Fuel economy for model year i and age j (mile/gal).
ef : Heat value of 1 kg of gasoline. E() i,j : Emission factor (g/mile).
i* : Baseline year (=1995).
PC BijBijBijUUU(),,,=+ () ( )
VMT(, i j ) EI() i, j BijPU(, )=×× g B UgFE(, i j ) EI i*,1 g ()
VMT(, i j ) × e f C (for energy ) BijU (), = FE(, i j ) VMT() i, j× E () i , j ( for emissions )
2.3.4 Maintenance
The maintenance phase accounts for the material production and manufacturing burdens of replacement parts. This phase includes both scheduled maintenance and unscheduled repairs. Unscheduled repairs are often associated with component failures while scheduled maintenance refers to preventive part replacements. The formulas to calculate the maintenance phase environmental burden BR(i,j), are shown as follows.
U BR : Lifetime environmental burden from the maintenance phase reported in the USAMP study.
VMT(i,j): Vehicle miles traveled for model year i and age j .
EIR (i,j) : Energy intensity of maintenance for calendar year i+j-1.
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λ(i,j) : Environmental burden from unscheduled repairs for model year i and age j.
i* : Baseline year (=1995).
VMT() i,, j EIR () i j U BijRR(, )=××+ Bλ () ij, β EI i*,1 R () β =120,000 miles
Although the USAMP study assumes that replacement parts come from OEMs, some of the vehicle components, such as catalytic converters, tires and fluids often are supplied from the recycling market (USAMP 1999). Thus, the environmental burden
U BR might have been slightly overestimated. Unscheduled repairs are assumed to occur after 120,000 miles of driving for modeling purposes.
2.3.5 End-of-Life
The end-of-life environmental burdens depend on materials use and energy intensity parameters. The end-of-life phase consists of four components: transportation of the used car to a dismantling facility; dismantling; shredding; and disposal of the shredder residue. It is assumed that the end-of-life environmental burden is proportional to the total weight of the vehicle. The environmental burdens are modeled based on the following notation and equation.
U BE : End-of-life environmental burden by the USAMP study.
EIE (i,j) : Energy intensity for calendar year i+j-1 when the model year i vehicle retires.
m (i) : Average total weight of a model year i vehicle. T i* : Baseline year (=1995).
mi() EI() i, j T E U BijEE(, )=×∗ × B mi() EI i*,1 T E ()
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2.4 Dynamic LCI Parameters for a Generic Vehicle
Modeling characteristics of the dynamic LCI parameters of generic mid-sized cars, including historical trends, future forecasts, and major assumptions are provided in this section.
2.4.1 Recycled Content
The average recycled content in the ferrous and aluminum materials for model year 1994 cars was estimated to be 33% (Keoleian et al. 1997). Material recycling can reduce environmental burdens by avoiding energy intensive processes associated with primary material production9. For instance, the energy consumption required to produce 1 kg of primary cast aluminum is reported to be 208.9 MJ, compared with 37.5 MJ for the recycled cast aluminum. Thus, the production of automotive cast aluminum with typical 85% recycled content consumes approximately 63.2 MJ/kg of energy (Aluminum Association 2000). The recycled content for each material or vehicle part largely depends on the status of recycling technology and infrastructure. The greatest technical hurdle of a recycling process is to economically remove impurities that can degrade the quality of recycled materials. For instance, the copper impurities in recycled ferrous materials and the paint residues in recycled plastic resin are major obstacles in automotive applications. In order to solve these problems, recycled materials are either mixed with primary materials or used as low-grade materials. Having a large enough supply of the same kind of scrap material is also critical for building recycling infrastructure. This study assumes that the recycled content of automotive materials is constant between 1985 and 2020 except for the case of aluminum. The recycling technologies and
9 More specifically, in this section, post-consumer recycled content is discussed. Post-industrial recycling is considered to be an efficiency issue in the manufacturing process, and is included in the energy intensity of the materials production phase in Section 3.4.2.1.
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infrastructure for ferrous scraps has been well established since at least the 1980s10. In fact, steel-making processes require significant amounts of steel scraps. Recycled scrap accounts for 20-30% of the raw materials for the Basic Oxygen Furnace (BOF) steel- making process, which produces hot-rolled, cold-rolled, and galvanized coils. The Electric Arc Furnace (EAF) steel-making technology, which produces mostly steel bar, uses recycled scrap as the sole raw material.11 Currently, the BOF provides 60% of US steel and the EAF accounts for the other 40%. Although EAF technology seems to increase at the expense of the BOF, it is uncertain whether EAF will emerge as the major steel process in the near future. Moreover, with current technologies, the steel recovery ratio from mixed scrap seems to have reached its maximum current feasibility (AISI 1998). It seems reasonable, therefore, to assume that the ratio between the BOF and EAF technology will not change in the foreseeable future without a dramatic change in scrap supplies. In addition, the future of recycled content in automotive plastics seems uncertain. Although some recycled plastics are used in automobiles, they have been limited to a small number of parts, including lamp housings, knobs, and carpets due to the lower quality compared to virgin materials (Keoleian et al. 1997). The cheap virgin materials associated with cheap oil prices also reduce the economic incentives for recycled resins. Furthermore, it is often difficult to collect large enough quantities of specific plastics needed for an economic recycling business due to the many varieties of types and grades of primary plastics and plastic composites. These obstacles could be partially relieved in the near future by the commitments of OEMs or by EPA or state-led initiatives that would issue regulations restricting Automotive Shredder Residue (ASR) (Staudinger and
10 A 1987 census reported 6075 establishments of vehicle wreckers/dismantlers in the US. 11 The recycled scrap includes the scrap generated within steel plants (Home Scrap), the scrap generated during the manufacture of steel products (Prompt scrap), and the steel scrap from post-consumer products (Obsolete Scrap).
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Keoleian 2001). However, ASR reductions through facilitating recycling practices may not lead to an increase in recycled contents in vehicle plastic materials, because recycled plastics from vehicle scrap may be used in non-automotive products. On the other hand, a significant improvement is expected for the recycled content in automotive aluminum. For model year 1995 cars, automotive cast aluminum included 85% recycled content while rolled and extruded auto parts included only 11% recycled aluminum (Aluminum Association 2000). As aluminum materials become more popular for auto parts due to weight saving effects12, more aluminum scrap will be recovered from these auto parts in the future. An abundant supply of scrap aluminum may also support aluminum recycling in the future. The Aluminum Association forecasts that, by the 2010s, 48% recycled content will be achieved for rolled and extruded aluminum while the recycled content of cast aluminum will drop to 81% (Aluminum Association 2000). If this scenario is valid, the average life cycle energy consumption for producing 1 kg of automotive aluminum will decrease from 90.9 MJ to 77.1 MJ13. These changes in recycled content of aluminum parts can be represented in the recycled content index, RI(a,i) (=R(a,i)/R(a,1995)). (See Section 2.3.1.) In this study, RI(Al,1995) and RI(Al,2020) each correspond to 1 and 0.848 (=77.1/90.9) assuming the Aluminum Association scenario will be achieved in 2020. The recycled content is assumed to linearly increase during the period. On the other hand, it is assumed that the past trends of recycled contents between 1985 and 1995 have been constant (RI(Al,1985),…, RI(Al,1995)=1).
12 The aluminum use in an average US-built car increased from 62.6 to 111.4 kg between 1985 and 2000, and is expected to increase to 115.8 kg by 2009 (Binder 2000; OSAT 2000). 13 It is assumed that 73.8% cast, 22.8% extruded, and 3.4% rolled aluminum is used in automotive applications (USAMP 1999).
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2.4.2 Materials Use
The changes in types and amounts of materials used will result in different environmental profiles for each model year car. For example, the increasing use of automotive aluminum, on the one hand, improves fuel economy due to weight savings, and on the other hand, consumes more energy than steel during the materials production phase. As shown in Figure 2.2, the technology improvement and the regulatory/socioeconomic factors affect materials use trends. For instance, the underlying reasons for continued usage of steel (steel accounts for 54%, by weight, of the average US built cars in 2000) are new coating and alloying technologies together with cost advantages. Research and development since the mid 1980s have made steel bodies more rust resistant, thinner, and lighter. Steel is also far less expensive than other lighter alternatives, especially in high-volume production (Binder 2000). ‘Ward's Automotive Yearbook’ is the main source of materials use data between 1985 and 2000. This series includes annual reports on the estimation of average materials consumption in US-built cars14(Binder 2000). Two different sources were used to produce the data for the future forecast between 2001 and 2020: "Delphi 10" by the University of Michigan Transportation Research Institute (UMTRI) and "On the Road in 2020" by the Energy Laboratory, MIT. These reports estimate the materials composition of model year 2004, 2009 and 2020 based on the forecasts of CAFE standards, material costs and technological improvements. For instance, Delphi 10 forecasts a 10% reduction of iron and steel use by 2009 if the CAFE standard is set at 30 mpg15, and a 20% reduction if it is set at 35 mpg (OSAT 2000). Table B1 in Appendix B summarizes
14 This series has been reporting the estimation of material in a “Typical Family Vehicle” since the 1995 edition. The terms ‘U.S.-built car’ and ‘Typical Family Vehicle’ are interchangeable in this series. 15 The forecast of 30-mpg CAFE standards by 2009 was selected in this modeling to be consistent with the Annual Energy Outlook report.
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the estimated materials composition used in this study. Linear increase or decrease is assumed to fill the data gaps between the major forecasts of 2004, 2009 and 2020.
2.4.3 Vehicle Miles Traveled (VMT)
VMT for an individual vehicle varies depending on the vehicle’s purpose, age, type, etc. According to the National Personal Transportation Survey (NPTS), the average annual miles per household vehicle in 1995 is 12,200 miles from self-reported data and 11,800 miles from odometer-based data. In a detailed analysis of the survey, a primary vehicle of a three-car household was driven, on average, 18,853 miles per year while secondary and tertiary vehicles were driven 9,806 and 4,555 miles respectively (FHWA 2001). The VMT of a fleet vehicle depends heavily on the establishments to which the vehicle belongs. For example, cars owned by the business sector were driven on average 22,780 miles, while government cars were driven on average 12,895 miles in 2000 (DOE 2002). In Chapter 3, the baseline VMT is assumed to be 12,000 miles per year, and the VMT effects on the optimal vehicle lifetimes are explored using 6,000 and 24,000 miles of VMT scenarios. In addition, a VMT may decrease with vehicle age or mileage because of deterioration, reduced reliability, and changes in vehicle purpose (e.g., from primary to secondary household vehicle). The annual average VMT with vehicle age has been studied in conjunction with the MOBILE6 emission model (EPA 1998; EPA 1999). The details regarding the age effects are described in Chapter 4.
2.4.4 Energy Intensity
The ratio between the total energy consumption and gross domestic product (GDP) is a traditional indicator of the energy intensity for an economic system. For the manufacturing energy intensity, ‘value of shipments’ or ‘value added’ has been used instead of GDP. However, these economic measures are subject to change according to
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inflation and prices (Hyman and Reed 1995). This study attempts to use physical units rather than economic units in measuring manufacturing outputs. The energy intensities based on economic values, however, are used as surrogates for physical units when physical measurements of outputs are inappropriate or infeasible. The manufacturing energy intensity indexes, which represent the ratios between the manufacturing energy intensity of a given year and the baseline year, 1995, are developed between 1985 and 2020 for each life cycle phase. Table B2 in Appendix gives the energy intensity indexes for each life cycle phase.
2.4.4.1 Materials Production
The materials production phase of a vehicle life cycle is associated with numerous materials processing sectors, including the steel, aluminum and petrochemical industries. Moreover, raw materials acquisitions and materials transportation are often included in the materials production life cycle phase, which relates to infrastructures such as the public utility, transportation and mining sectors. This study primarily focuses on energy intensities of major auto materials processing such as ferrous materials, aluminum, and plastics production. Detailed activities associated with raw materials acquisition and transportation activities are assumed to be included in the materials processing. Different approaches are adopted for past trends (1985-1998)16 and future forecasts (1999-2020). The procedures to estimate the past trends are described in the following sections.
2.4.4.1.1 Historical Trends
Ferrous materials together with aluminum constitute more than 70% of vehicle weight (Keoleian et al. 1997; Binder 2000). Table 2.2 shows the life cycle energy consumption of steel and aluminum materials production. The energy intensity for
16 The ranges are simply based on data availability.
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primary aluminum processing without any recycled content is assumed to represent the manufacturing energy intensity of the aluminum industry17.
Table 2.2: Life cycle energy consumption for producing 1 kg of materials (MJ) Steel Products Automotive Aluminum Hot Rolled Cold Rolled Galvanized EAF Cast Extruded Rolled 26 28 32 9.4 56 190 183 Source: (USAMP 1999)
The detailed steps for modeling manufacturing energy intensities of ferrous and aluminum materials are presented below.
1. Obtain the total US net shipments of products for each year. Table 2.3 shows the total US net shipments of steel mill products and primary aluminum production from 1985 through 1998 for select years.
Table 2.3: Net shipments of steel mill products and primary aluminum production in the US
Production 1985 1988 1991 1994 1998 Iron and Steel Net Shipments 66.2 76.0 71.5 86.3 92.6 (Million metric tons) Primary Aluminum Production 3500 3944 4121 3299 3713 (Thousand metric tons) Source: (AISI 1998; USGS 2000)
2. Obtain the total inputs of energy by the steel and aluminum industries. The most comprehensive data source on the manufacturing energy input is the Manufacturing Energy Consumption Survey (MECS) by the Energy Information Administration
17 Assumption of no recycled content is made to make comparisons between years consistent.
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(EIA). The surveys were conducted for reporting years 1985, 1988, 1991, 1994 and 1998. The surveys include the total input of energy for heat, power, and electricity generation to the steel and aluminum industries (EIA 1997). This does not include some energy inputs in the form of feedstock or raw material. Tables 2.4 and 2.5 show the data for the available years. The MECS uses the Standard Industrial Classification (SIC) system to categorize businesses by type of activities. According to the SIC system, the industry categories for Tables 2.4 and 2.5 correspond to the industry group of SIC 331 (Blast Furnace and Basic Steel Products) and SIC 3334 (Primary Aluminum). The industry group of SIC 331 includes industries such as SIC 3312 (Steel works, blast furnaces including coke ovens, rolling mills) and SIC 3313 (Electrometallurgical products, except steel). According to the 1994 survey, the total energy inputs to SIC 3312 and 3313 correspond to 97% of the inputs to the
Table 2.4: Total inputs of energy for heat, power, and electricity generation by iron and steel industries (SIC 331) Type of Energy 1985a 1988a 1991b 1994 1998c Net Electricity (million kWh) 47730 49658 46751 53269 56478 Residual Fuel Oil (1000 barrels) 5475 5772 5002 6680 5016 Distillate Fuel Oil (1000 barrels) 961 1066 921 1068 1020 Natural Gas (billion cu ft) 435 463 421 503 479 LPG (1000 barrels) 125 301 78 104 130 Coal (1000 short tons) 2337 1684 1151 1711 2141 Coke and Breeze (1000 short tons) 21856 29987 21690 26503 24033 Other (trillion Btu)d 486 467 450 475 390 a Estimated from SIC 3312 (Steel works, blast furnaces including coke ovens, rolling mills). b Estimated from SIC 3312 and 3313 (Electrometallurgical products, except steel). c Sum of the data for NAICS 331111(Iron and Steel Mills), 331112 (Electrometallurgical Ferroalloy Products), and 3312 (Steel Product Manufacturing from Purchased Steel). d Primarily byproducts of the production of coke from coal, such as coke oven gas. Source: (EIA 1988; EIA 1991; EIA 1994; EIA 1997; EIA 2001)
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Table 2.5: Total inputs of energy for heat, power, and electricity generation by primary aluminum production (SIC 3334) Type of Energy 1985 1988 1991 1994 1998a Net Electricity (million kWh) 61648 65818 67317 53552 57355 Residual Fuel Oil (1000 barrels) W 2 * * * Distillate Fuel Oil (1000 barrels) 52 74 127 125 * Natural Gas (billion cu ft) W 19 20 16 14 LPG (1000 barrels) W 2 42 W * Other (trillion Btu) W 1 1 W 1 a NAICS 331312 (primary aluminum) is used. *Estimated to be less than 0.5. W=Withheld to avoid disclosing data for individual establishments. Source: (EIA 1988; EIA 1991; EIA 1994; EIA 1997; EIA 2001)
SIC 331. If the data for SIC 331 are not available, the values for SIC 3312 and 3313 are extrapolated to estimate the total energy inputs to the Iron and Steel industry. For the year 1998, the values classified by NAICS18 (North American Industry Classification System) are used as surrogates.
3. Estimate the primary energy consumption for materials production. This step converts energy input to primary energy consumption, which accounts for the total fuel cycle of each energy source. For example, the conversion of the electrical power into primary energy takes into account the combustion efficiencies of fuels, the pre-combustion energies for the fuels, and the line losses from the electricity transmissions. Table 2.6 gives the primary energy consumptions per unit energy sources. Tables 2.7 and 2.8 show estimated total primary consumption for steel and aluminum industries. In order to determine the total primary consumption, the
18 On April 1997, the US adopted NAICS as the industry classification system to replace the SIC system (U.S. Census Bureau 2002).
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missing data marked 'W' and '*' in Table 2.5 are estimated by linear weighting methods based on the known values.
Table 2.6: Energy conversion efficiencies of major energy types
Primary Energy Heat Content Type of Energy Consumption Efficiency (103 BTU) (103 BTU) Net Electricity (1,000 kWh) 3412 10621 0.321 Residual Fuel Oil (1,000 barrel) 6300 7182 0.877 Distillate Fuel Oil (1,000 barrel) 5838 6636 0.880 Natural Gas (109 cubic feet) 1.03E+06 1.16E+06 0.888 LPG (1,000 barrel) 4011 4536 0.884 Coal (1,000 short ton) 22400 22800 0.982 Coke and Breeze (1,000 short ton) 24800 28200 0.879 Other - - 0.899*
*Average value of efficiency excluding Net Electricity Source: (DOE 2000; EIA 2000)
Table 2.7: Estimated primary energy consumption for steel industry (1012 Btu) Type of Energy 1985 1988 1991 1994 1998 Net Electricity 507 527 497 566 600 Residual Fuel Oil 39 41 36 48 36 Distillate Fuel Oil 6 7 6 7 7 Natural Gas 505 536 488 583 556 LPG 0.6 1.4 0.4 0.5 0.6 Coal 53 38 26 39 49 Coke and Breeze 616 846 612 747 678 Other 541 519 501 528 434 Total 2269 2517 2166 2520 2359
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Table 2.8: Estimated primary energy consumption for primary aluminum industry (1012 Btu) Type of Energy 1985 1988 1991 1994 1998 Net Electricity 655 699 715 569 609 Residual Fuel Oil 0.01 0.01 0.01 0.01 0.01 Distillate Fuel Oil 0.4 0.5 0.8 0.8 0.9 Natural Gas 21 22 23 19 16 LPG 0.009 0.009 0.2 0.2 0.2 Other 0.8 0.9 0.9 0.7 0.8 Total 677 723 740 589 627
4. Estimate the energy intensity per kg of steel and primary aluminum production. The total primary energy data estimated in Tables 2.7 and 2.8 are divided by the total annual production given in Table 2.3. The energy intensities are represented as mega joule per kilogram of materials production. Table 2.9 gives ‘the manufacturing energy intensities’ and ‘the manufacturing energy intensity indexes’ for the steel and primary aluminum production based on a baseline year 199819.
Table 2.9: Estimated energy intensities and indexes for the steel and primary aluminum production (excludes some upstream processes)
Industry Unit 1985 1988 1991 1994 1998 MJ/kg 36.1 34.9 32.0 30.8 26.9 Iron and Steel Index 1.35 1.31 1.19 1.15 1.00 (1998=1)
MJ/kg 204 193 190 188 178 Primary Aluminum Index 1.14 1.08 1.06 1.06 1.00 (1998=1)
19 The manufacturing energy intensity indexes are eventually determined using 1995 as the baseline.
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The energy intensities obtained here do not consider the energy consumption for raw materials acquisition or transportation energy consumption required between processes. Although the energy intensities in Table 2.9 may be slightly underestimated due to these exclusions, these results generally agree with other studies. Ecobalance reported 32 MJ/kg and 26 MJ/kg of primary energy consumption for galvanized and hot rolled steel products (USAMP 1999). A study supported by the Department of Energy (DOE) estimated that the energy intensities for the US steel industry in 1985, 1990 and 1998 were 25, 20, and 18 MBtu/ton (= 29, 23, and 21 MJ/kg) (Stubbles 2000). A recent study of the Aluminum Association estimates the primary energy consumption of primary cast aluminum to be 209 MJ/kg (Aluminum Association 2000). Different conversion factors, data sources and classification standards between NAICS and SIC might be the sources of discrepancies between the studies. Since the chemical processes, including plastic compounding, involve a variety of organic intermediates, the energy intensity primarily depends on the structural change of products or qualities of raw materials such as crude oils. Energy intensity20 trends in the US chemical industry have been flat since the late 1980s, when inexpensive energy sources for heat, power, and feedstocks became available (DOE 2000). In fact, the EIA estimates that the manufacturing energy intensity of the US chemical industry increased slightly21 between 1985 and 1994 (EIA 1998). Based on this information, this study assumes that the energy intensities of plastic materials productions were constant between 1985 and 1998. Other materials, such as brass, rubber, glass and fluids comprise about 20% of vehicle materials. The energy intensity of a given year for producing these materials is
20 The energy intensity trends were based on economic values rather than physical measures. 21 Increasing from 11.09 to 12.14 thousand Btu/1992 constant dollars. Source: "Table 4. Energy-Intensity Ratio of Total Inputs of Energy per Value of Shipments by Major Manufacturing Groups for 1985, 1988, 1991, and 1994": The energy-intensity ratios that have been adjusted to the mix of products shipped from manufacturing establishments in 1985 (EIA 1998)
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assumed to be the mean of the three energy intensities for producing ferrous metals, aluminum, and plastics.
2.4.4.1.2 Future Forecasts
The energy intensity of a material’s production is limited by practical or theoretical maximums of process efficiencies. Fruehan estimates the practical minimum energy requirement for producing hot-rolled steel is only 1-2% 22 of the current energy consumption while that for producing cold-rolled steel is 38-45%23of the current practice (Fruehan et al. 2000). Thermodynamic minimum energy intensities for aluminum and steel materials are reported to be 29.3 GJ/tonne and 7.1 GJ/tonne compared with the actual intensities of 88 GJ/tonne and 33.1 GJ/tonne24 in 1980 (Ross 1987). The Aluminum Association assessed the ‘Improvement Potential’ in primary aluminum processing for the North American Aluminum Industry based on 1995 performances. According to its reports, the smelting process, which consumes more than 50% of the energy for the primary aluminum process, contains only 2.1% improvement potential. In contrast, the alumina refining process, which constitutes around 10% of the energy for primary aluminum production, contains 39.1% improvement potential. Other processes, including bauxite mining, anode production and primary ingot castings, show 3-9% improvement potential (Aluminum Association 2000). Future trends of energy intensity for materials production are estimated primarily based on the Annual Energy Outlook 2001 report from the Energy Information Administration (EIA). In this report, the energy intensities are represented by ‘Energy
22 For the cold rolling process, the actual energy requirement is 1.0-1.4 GJ/t compared with 0.02 GJ/t of practical minimum. 23 For the hot rolling process, the actual energy requirement is 2.0-2.4 GJ/t compared with the 0.9 GJ/t of practical minimum. 24 These values do not include upstream energy requirements and consider only primary aluminum.
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Consumption per Unit of Output (1000 Btu/1992 dollars of output)25’ (EIA 2001). Table 2.10 shows the future forecast of energy intensities for the selected materials production industries based on the Annual Energy Outlook 2001. For the materials industries not specified in Table 2.10, average profiles of the three industries are used. Table B2 in Appendix B gives the complete energy intensity indexes between 1985 and 2020. It shows a 32% and 17% decrease in the energy intensity of steel and aluminum production respectively, between 1995 and 2020.
Table 2.10: Forecast of energy consumption per unit of output (1000 Btu/1992 dollar of output) in industrial sectors
Iron and Steel Aluminum Bulk Chemical Industrial Sector Year Industries Industries Industries Average 2000 22.91 11.24 36.8 7.31 2005 20.91 10.76 36.2 6.86 2010 19.58 10.36 35.7 6.31 2015 18.56 10.02 35.3 5.82 2020 17.77 9.77 34.9 5.36 Source: (EIA 1998; EIA 2001)
2.4.4.2 Manufacturing
The average energy intensities of ‘all’ industries26 are used to represent the energy intensity of manufacturing a vehicle. The Manufacturing Energy Consumption Surveys (MECS) 1994 has been used as a primary source for past trends (1985-1994), while the Annual Energy Outlook 2001 report has been used for future forecasts (1998-2020) (EIA 1998; EIA 2001). The values between 1994 and 1998 have been estimated using older reports, such as the Annual Energy Outlook 1998 and Annual Energy Review 99. Table
25 Energy intensities based on economic measures are, however, subject to inflation and change of prices. 26 ‘All’ industrial sectors from SIC 20 to 39.
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B2 (Appendix B) includes the manufacturing energy intensity indexes for the manufacturing life cycle phase, using 1995 as a baseline.
2.4.4.3 Use
The manufacturing energy intensity associated with vehicle use is the refining energy intensity. It is assumed that the energy intensities of the refining industry (SIC 2911) were flat between 1985 and 1998. As discussed in Section 2.3.4.1.1, this is based on information that the energy efficiencies of the US chemical industries have been stagnant since the late 1980s. On the other hand, the Annual Outlook 2001 has been used to forecast future energy intensities for gasoline production. The future forecast of Annual Energy Outlook 2001 also estimates the energy intensities of refining industries to be nearly constant through 2020 (EIA 2001).
2.4.4.4 Maintenance
Maintenance of a vehicle is associated with a variety of industrial sectors. In addition to the activities in repair shops, the life cycles of replaced vehicle parts, such as materials production, manufacturing, and transportation of the replaced parts are involved. As is the case for the manufacturing life cycle phase, the average energy intensity of ‘all’ industries has been used as proxies for the energy intensity of the maintenance phase. This assumption should not introduce a significant error, considering the small fraction of life cycle energy consumption (1.7%) of this phase (USAMP 1999).
2.4.4.5 End-of-Life
The end-of-life environmental burdens of vehicle life cycles are associated with dismantling, shredding and transportation of retired vehicles. The majority (66%) of energy for this life cycle phase is consumed during transportation of retired vehicles.
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Used cars are typically transported to dismantling facilities by trucks (USAMP 1999). Thus, energy intensities for the vehicle (truck) use phase (developed in Section 2.3.4.3 for refining industry) have been used as surrogates for the energy intensities of the end- of-life stage.
2.4.5 Fuel Economy
The fuel economy of a new car is estimated in laboratory tests required by the US Environmental Protection Agency (EPA). There are two different fuel economy test cycles, one for city driving and the other for highway driving. Based on the laboratory tests, the “composite 55/45 combined mpg (hereafter referred to as composite fuel economy)” is calculated using the following equation (EPA 2000).
MPG55/45 = 1 / (0.55/ MPGC + 0.45/MPGH) MPGC: Fuel economy on the EPA City Driving cycle MPGH: Fuel economy on the EPA Highway Driving cycle
The CAFE regulations apply to the sales-weighted composite fuel economies determined by this procedure. The composite fuel economies based on the laboratory tests, however, are significantly higher than the real world fuel economies. In the Fuel Economy Guide published each year by the EPA, the laboratory fuel economies are adjusted for in-use fuel economies27: the city fuel economies are multiplied by 0.9 and the highway fuel economies by 0.78 (EPA 2000). The USAMP study also used adjusted fuel economies for three vehicle modelsTaurus, Intrepid, and Lumina. The study calculated adjusted composite fuel economies using the in-use City and Highway fuel economies rather than the laboratory City and Highway fuel economies (USAMP 1999).
27 These fuel economies are used for the fuel economy labels on new vehicles.
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The adjusted composite fuel economy of the generic vehicle in the USAMP study was 22.8 mpg28, while that of an average model year 1995 car was 24.2 mpg29. This suggests that the generic vehicle defined in the USAMP project was less efficient than the average 1995 model cars. The generic vehicle represented a mid-sized model year 1995 family sedana hypothetical hybrid of the Ford Taurus, Dodge Intrepid, and Chevrolet Lumina, all produced in North America. As discussed in 2.2, the dynamic LCIs for this study describes the dynamics of environmental burdens based on the generic vehicle defined in the USAMP study. On the other hand, the sales-weighted composite fuel economy based on laboratory tests for the model year 1995 was 28.3 mpg (EPA 2000). In order to estimate the fuel economies for dynamic LCIs, the sales-weighted composite fuel economies based on laboratory tests are converted to in-use composite fuel economies. Figure 2.4 shows the sales-weighted composite fuel economy trends and forecasts based on laboratory tests. When used as a dynamic LCI parameter, these fuel economies are multiplied by the ratio 0.806 (= 22.8/28.3) that represents the difference between the in-use fuel economies of the USAMP generic vehicle and the sales-weighted 1995 average car. Fuel economy can decline with vehicle mileage due to the deterioration of engine systems as well as other maintenance-related reasons, such as deflation of tires. In fact, a statistical study describes fuel economies as deteriorating with vehicle mileage in severe urban driving conditions (Ang et al. 1991). However, there exists little evidence to generalize the deterioration behavior according to a study based on the EPA’s long-term FTP survey (Austin and Ross 2001). Thus, it is assumed that fuel economy does not decline as vehicle mileage increases.
28 For the Taurus and Lumina, 20 and 29 mpg of the City and Highway in-use fuel economy, and for the Intrepid, 19 and 27 mpg of the City and Highway in-use fuel economy, have been used. 29 For model year 1995 cars, an average of 23.6 mpg City and 37.6 mpg Highway laboratory fuel economy has been used.
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32
30
28 Fuel Economy (mile/gal) Economy Fuel
26
1990 2000 2010 2020 Model Years
Figure 2.4: Fuel economy trends and forecast between 1985 and 202030 (EPA 2000; EIA 2001)
2.4.6 Emission Factors
2.4.6.1 CO2
The CO2 emission from vehicle use depends directly on the mass of fuel combusted since the carbon content in the fuel determines the CO2 emissions. The method for calculating per-mile CO2 emissions for a gasoline vehicle has been formulated as follows31.
30 Between 1985 and 2000, values represent the composite laboratory fuel economy values, which is the sales-weighted average for the year. Between 2001 and 2020, the “Energy Efficiency Indicators of New Car” in Annual Energy Outlook 2001 were used. 31 The major assumptions for this method include 99% oxidation of gasoline during combustion, 0.740kg/l (=2.80kg/gal) of gasoline density, and CH1.9 of average gasoline composition (De Lucchi 1991; Bosch 2000).
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E i,j=× Mass of CO per unit C C content in gasoline CO2 ( ) ( 2 ) ( ) ××()()Gasoline consumption per mile Fuel combustion ratio
44 12 1 =××2.80kg/gal × × 0.99 12 13.9 FE() i,j
1 =×8.77kg/gal () FE() i,j E(i,j) :Emission factor per mile (g/mi) for model year i and vehicle age j FE(i,j): Fuel economy (mile/gal) for model year i and vehicle age j
2.4.6.2 Regulated Emissions (HC, CO, and NOx)
The in-use gpm (grams per mile) emissions account for not only the FTP type emissions but also for the emissions from driving conditions that differ from the FTP, and these are often called off-cycle emissions. In particular, evaporation is a significant emission source for HC. In this study, the FTP type emissions represent the expected emissions from generic cars with different emission performances. Thus, the FTP type emissions,
EFTP(g/mi), consist of both the normal-emitter (EN(g/mi)) and high-emitter (EH(g/mi)) contributions.
EEEFTP=+ N H =×+×FPFP NN HH =−+×FPFPNHHH()1
FFPNHH+×
Both normal- and high-emitter contributions to EFTP are modeled as products of ‘emission factor,’ F(g/mi), and ‘probability of occurrence,’ P. Since the probability of a high- emitter occurrence, PH, is small, the normal-emitter contributions, EN (g/mi) are approximately the same as the emission factors of normal-emitters, FN (g/mi). On the
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other hand, the high-emitter contributions are the products of ‘high-emitter emission factors,’ FH(g/mi), and ‘probability of high-emitter occurrence,’ PH. Therefore, the gpm emissions factor of a generic vehicle, E (g/mi), is defined by the sum of contributions from FTP normal emitter EN, plus the incremental emissions:
FTP high emitter EH , non-FTP or off-cycle driving Eoff, and non-FTP evaporation Eevap.
EE=+FTP E Non- FTP
ENHoff++E E ( for CO and NOx ) = EEEN++ H off + E evap ( forHC )
Although both EN and EH are modeled to be functions of model year and age, the off-cycle and evaporative emissions are assumed to be independent of vehicle mileage since the emissions sources are more sensitive to driving patterns and climates rather than vehicle condition (Ross et al. 1995).
2.4.6.2.1 FTP Normal-Emissions (EN)
This study primarily relies on a separate study by Austin and Ross for historical trends of the regulated emissions from FTP normal-emitters, EN (Austin and Ross 2001). In the Austin and Ross study, the emissions from normal-emitters were determined by multiple databases of the FTP-type driving tests. The EPA's long-term in-use emissions survey was used as the primary source of the data. The University of California Riverside’s Comprehensive Modal Emission Model (CMEM) database and California Air Resources Board (CARB)’s Light Duty Vehicle Surveillance Program (LDVSP14) were also used as supplemental sources. Table 2.11 summarizes the emission factors of normal-emitters when measured by the FTP. The emission factors are then formulated as linear functions of mileage for each model year.
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Table 2.11: Normal-emitter's FTP type emission factors (EN) with model year and mileage Emissions at Specified Vehicle Mileage (g/mi) Emissions Model Year 30K 50K 80K 1985 0.35 0.47 0.65 HC 1990 0.23 0.29 0.37 1995 0.16 0.18 0.21 1985 3.87 5.02 6.75 CO 1990 3.12 3.79 4.79 1995 1.89 2.11 2.43 1985 0.76 0.82 0.92 NOx 1990 0.57 0.64 0.74 1995 0.28 0.37 0.51 Source: (Austin and Ross 2001)
Since 1975, the emission reductions of regulated pollutants in the US have largely been driven by federal emission regulations. OEMs take into consideration economic costs and benefits in order to meet regulations at optimal compliance margins. In determining optimal compliance margins, OEMs consider the levels of variation observed in emissions testing along with a degree of statistical confidence (Cullen 2001). This compliance margin is often represented by a headroom value, which is defined as follows.
Emissions Standard− Certification Value Headroom(%)=× 100 Emissions Standard
Table 2.12 provides the headroom values as well as the federal emission standards that are used for the future forecasts. In this study, a headroom of 30% for 50K miles and
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40% for 100K and 120K miles have been selected as baselines32 for future forecasts. These baselines primarily apply to HC and NOx emissions, which are the main targets of the Tier 1 and 2 federal regulations33. The CO standards are not strictly controlled under Tiers 1 and 2 since the CO pollution in most urban areas is no longer significant. Thus, the emissions control system designed to comply with the HC and NOx standards may overachieve on CO controls (Cullen 2001). For the same reason, the CO headroom will increase as the standard levels of other pollutants become stricter.
Table 2.12: Federal Certification Exhaust Emission Standards for light duty vehicles (g/mi) and estimated headroom values (%) for cars (EPA 2000; EPA 2000; Cullen 2001) CO NMHC34 NOx Regulations 50K 100K 120K 50K 100K 120K 50K 100K 120K
3.4 - - 0.34 - - 1.0 - - Tier 0a (-) (-) (-) (-) (-) (-) (-) (-) (-)
3.4 4.2 - 0.25 0.31 - 0.4 0.6 - Tier 1b (40) (50) (-) (30) (40) (-) (30) (40) (-)
3.4 - 4.2 0.075d - 0.090d 0.05 - 0.07 Tier 2c (50) (-) (55) (30) (-) (40) (30) (-) (40) a Tier 0 applied before 1993 b Tier 1 phased in between 1994 and 1998 and is currently in effect c Bin 5 standards. Tier 2 will phase in between 2004 and 2007 for light duty vehicles. d NMOG35 standard
32 This is based on a recommendation of General Motor's Powertrain department (Cullen 2001). 33 An emissions control system that is engineered to comply on HC and NOx may overachieve on CO control. 34 A HC level can be derived from NMHC by multiplying the conversion factor 1.206 (= 0.41/0.34). This factor represents the ratio between the total hydrocarbon (THC) and NMHC level of the Tier 0 standard. 35 Non-Methane Organic Gases. The conversion factor from NMHC to NMOG is 1.04 for a gasoline vehicle (EPA 2000).
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It is assumed that there will be further reductions in HC and NOx emission standard levels after the Tier 2 program is fully in effect in 2009 (Cullen 2001). The standard levels of 2020 (Tier 3) will be half of the Tier 2 standard level. While the CO standard will not change, the headroom will increase to 55% for 50K and 60% for 120K until 2020, due to enhanced performance of emission controls targeting HC and NOx pollutants. Based on the historical trends in Table 2.11 and the future forecasts in Table 2.12, the FTP normal emissions (EN) are modeled as linear functions of age for each model year.
2.4.6.2.2 FTP high-Emissions (EH)
Since it is difficult to recruit high-emitters for the EPA's long-term in-use emissions survey, their survey may not precisely estimate the high-emitter contribution to
10 HC y = 140159e-0.1229x NOx CO
1 y = 7.5372e-0.0353x
0.1 Emissions (g/mi)
y = 44278e-0.1402x
0.01 85 87 89 91 93 95 97 99 Model Years
Figure 2.5: Estimation of the average (at 80K miles) emission factor contributions of * high-emitters (E H) (Austin and Ross 2001)
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average vehicle emission factors. For high-emitter emissions, the "random-sample full- test" IM240 data of Wisconsin (1996-98) have been analyzed. As discussed in Chapter 1, the IM240 cycle is similar to the FTP cycle and a larger number of vehicles are tested than in other surveys. Figure 2.5 shows the gpm contribution of high-emitters at 80K miles. The lifetime average contribution of a high-emitter (at 80K miles) is noted here by
* E H (Austin and Ross 2001). The lines have been extrapolated from the specific data points in order to estimate the high-emitter emissions of model years not represented in the graph. The high-emitter contribution to the emission factors of a generic vehicle is then described as a function of mileage36, assuming the emissions are proportional to the odometer readings.
Mileage * EH =×EH 80k
High-emitter contributions (EH) to the emission factors (E) for model year 2000 through 2020 will be 25% of the emissions standards based on projections from past trends of FTP high-emissions (Figure 2.5).
2.4.6.2.3 Emissions from Off-Cycle Driving (Eoff)
Both the off-cycle and evaporative emissions are assumed to be independent of vehicle mileage since the emission sources may be sensitive to driving patterns and climates rather than vehicle condition (Austin and Ross 2001). The modeling of off- cycle emissions relies on a previous study regarding real-world automotive emissions (Ross and Wenzel 1998). Based on the study, the off-cycle emissions are modeled to be 2.8, 0.05, and 0.24 g/mi for CO, HC, and NOx between model years 1985 and 1999; 2.4, 0.036, and 0.1 g/mi between model years 2003 and 2020; and decreasing in a linear
36 Vehicle mileages are interchangeable with age if miles per year are provided.
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fashion between model years 1999 and 2003. These emissions are based on the assumption that the Supplemental Federal Test Procedure (SFTP) regulatory standards, which include high power driving, will be in effect in the early 2000s.
2.4.6.2.4 Evaporative Emissions (Eevap)
The Mobile Source Emission Factor Model (MOBILE), EPA's emission factor model, estimates 0.5 g/mi of evaporative HC emissions for model year 1993 and 0.37 g/mi for model year 2000 (EPA 1994). In this study, the evaporative HC emissions for the model years between 1985 and 1988 are assumed to have been 1 g/mi and are assumed to have decreased linearly to 0.5 g/mi until 1993. Also, it is assumed that evaporative HC emissions will decrease from 0.37 g/mi to 0.25 g/mi during the phase-in of the Tier 2 standard between 2004 and 2009, and will reach 0.15 g/mi in 202037.
2.4.7 Component Reliability
The component reliabilities for the present study rely on the scheduled maintenance data from the USAMP study. Table B3 in Appendix B presents the materials used for scheduled maintenance and service. Due to data limitations, unscheduled maintenance data are not considered, and the component reliability for each model year is assumed to be constant over vehicle age. This assumption would underestimate the environmental burdens from old vehicles. On the other hand, although some vehicle components, such as tires and fluids, are supplied from the reuse/recycling market, the USAMP study assumes that replacement parts come only from OEMs (USAMP 1999). By ignoring reuse/recycling environmental benefits, the overestimated
37 This is based on a report that analyzed the emissions inventory for Tier 2 standards using MOBILE5 predictions (EPA 1999).
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burdens from this assumption may partially offset the underestimation associated with component reliability. The maintenance environmental burdens during the life cycle of a generic vehicle are known to be insignificant (0.8-2.0 % for 120,000 miles of driving based on environmental burdens) (Sullivan et al. 1999; USAMP 1999). Thus, these assumptions are likely to have a negligible effect on simulations in Chapters 3 and 4.
2.5 Results and Discussion
Figures 2.6 and 2.7 present the dynamic LCI profiles of the materials production and manufacturing life cycle phases respectively, for generic vehicles with model years between 1985 and 2020. Alternative vehicle powertrains, such as fuel cells are not investigated in this dynamic LCI modeling. Depending on the pollutants, up to 40% emission reductions are expected between the model years 1985 and 2020. The fluctuations of environmental burdens in the early 1990s in the manufacturing phase (Figure 2.7) can be attributed to the changes in average vehicle weight and energy intensities of major industries such as aluminum and steel industries. Figure 2.8 shows the dynamic LCIs for the use phase based on driving 12,000 miles annually for select model years. As discussed before, CO2 emissions remain nearly constant with vehicle age for each model year, while the grams per mile levels of the regulated auto emissions (CO, NMHC, and NOx) become elevated with increasing age of vehicles. In particular, the regulated emissions from the 1980s model years increase sharply with vehicle age, primarily due to the low durability of emission control systems, including catalytic converters. Improved technology and stricter regulations have remarkably enhanced the durability as well as the initial performance of emission control systems for the 1990s model years (Austin and Ross 2001). Moreover, emission profiles
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for model years beyond 2000 predict that Tier 2 and further regulatory programs will additionally reduce emissions from both new and old vehicles. Figure 2.9 shows the dynamic LCIs for the maintenance phase for select model years, based on driving 12,000 miles annually. For each pollutant, up to 30% of emissions are expected to decrease between model years 1985 and 2020. As shown in Section 2.3.4, this progress will be achieved primarily through energy intensity improvements in the future.
10x103
60x103 8x103 NOx NMHC CO 6x103 40x103 CO2 right-hand scale
4x103 Emissions(g) Emissions (kg) Emissions
20x103
2x103
0 0 1985 1990 1995 2000 2005 2010 2015 2020
Model Years
Figure 2.6: Dynamic LCIs of materials production stage, BM(i), between model years 1985 and 2020
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10x103
60x103 NOx 8x103 NMHC CO
CO2 right-hand scale 6x103 40x103
4x103 Emissions (g) Emissions Emissions (kg)
20x103
2x103
0 0 1985 1990 1995 2000 2005 2010 2015 2020 Model Years
Figure 2.7: Dynamic LCIs of manufacturing stage, BA(i), between model years 1985 and 2020
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CO 500
MY 1985 MY 1995 400 MY 2005 MY 2015
300
200 Emissions (kg/Year)
100
0 5101520
Age (Year) NMHC 50
MY 1985 40 MY 1995 MY 2005 MY 2015
30
20 Emissions (kg/Year) Emissions
10
0 5 101520 Age (Year)
Figure 2.8: Dynamic LCIs of use stage, BU(i,j), for select model years based on driving 12,000 miles annually
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NOx 50
MY 1985 MY 1995 40 MY 2005 MY 2015
30
20 Emissions (kg/Year) Emissions
10
0 5101520
Age (Year)
CO2 7000
6000
5000
4000
Emissions (kg/Year) MY 1985 MY 1995 3000 MY 2005 MY 2015
2000 5101520
Age (Year)
Figure 2.8: Continued
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5000 100
4500
4000 80
3500
3000 60
2500
2000 NOx 40 Emissions (g) Emissions NMHC Emissions (kg) 1500 CO CO2 right-hand scale 1000 20
500
0 0 1985 1990 1995 2000 2005 2010 2015 2020
Model Years
Figure 2.9: Dynamic LCIs of maintenance stage, BR(i,j), at vehicle age 1 based on driving 12,000 miles annually
60
1000 250
800 200
600 150 NOx NMHC CO 400 100
Emissions (g) CO right-hand scale
2 Emissions (kg)
200 50
0 0 1985 1990 1995 2000 2005 2010 2015 2020 Model Years
Figure 2.10: Dynamic LCIs of the end-of-life stage, BE(i,j), based on retiring vehicles in the tenth year
Figure 2.10 presents the end-of-life environmental burdens based on retiring vehicles in the tenth year. Up to 15% of emission reductions for certain pollutants are expected between model year 1985 and 2020. The slight increase in emissions between mid-1990s and early 2000s model years may be associated with an increase in total vehicle weight (Section 2.3.5). (See Table B1 in the Appendix B.) Appendix A presents the complete dynamic LCI results for mid-sized generic vehicle.
61 2.6 Conclusion
This chapter presents the methods and results of dynamic Life Cycle Inventories (LCIs) for generic mid-sized passenger cars with internal combustion engines, between model years 1985 and 2020. Although the uncertainties associated with future forecasts are inevitable, the best available data were used to describe LCI parameters. Three dynamic LCI factors are defined to describe the changes in the life cycle environmental performance based on vehicle model year and age: regulatory/socioeconomic factors, technology improvements, and vehicle deterioration behavior. The LCI parameters quantify the evolution of these dynamic LCI factors. Finally, dynamic LCIs are determined as single-year environmental profiles for each life cycle stage. These LCIs are used as input data for the life cycle optimization model in Chapter 3 and for the fleet optimization model in Chapter 4. Findings regarding the dynamic LCI parameters reveal that, for 1980s model years, regulated emission factors will sharply increase as cars age, characterizing the deterioration behavior of those model years. The analysis in this chapter also shows that technology improvements and regulatory factors during the 1990s considerably reduced the regulated emission factors of both new and old cars. Parameters for energy and CO2 profiles show different trends from those for regulated emissions. In particular, deterioration behavior is unimportant for energy and CO2, since fuel economy is not likely to decrease with vehicle age. As will be seen in Chapters 3 and 4, these inconsistencies in parameters across different categories result in different characteristics of optimal lifetimes. Environmental profiles other than the vehicle use phase also change significantly with model year/age, and contribute to determining optimal lifetimes.
62 CHAPTER III
OPTIMAL VEHICLE LIFE BASED ON LIFE CYCLE OPTIMIZATION MODEL
3.1 Introduction
Evaluating the optimal life of an automobile poses a challenging resource and environmental management problem. Extending the service life of an existing automobile avoids the additional resource investments and environmental impacts associated with the production of a new vehicle. For example, the production of a mid- sized 1995 automobile was found to consume 125,000 MJ of primary energy (equivalent to 20 barrels of oil), 1,500 kg of iron ore, and 230 kg of bauxite, and generate 3,000 kg of solid waste and 210 kg of air pollutants (USAMP 1999). Product life extension is an important, well-recognized green engineering strategy, which can be achieved by enhancing vehicle durability and serviceability (EPA 1993; Stahel 1994). On the other hand, replacement of older, inefficient product with newer, more efficient product is another important mechanism for reducing environmental impacts. While product life extension limits manufacturing related impacts, adopting newer technology can enhance environmental performance during the product use stage. This chapter determines optimal product lifetimes using life cycle assessment (LCA), a comprehensive environmental measurement tool, and dynamic programming, an engineering optimization tool. To determine the optimum life of a product from an LCA perspective, a novel life cycle optimization (LCO) model is developed. The inputs to this mathematical model are the dynamic LCIs determined in Chapter 2. For demonstration of the LCO model, optimizations of the vehicle replacement decision for
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mid-sized passenger car models between 1985 and 2020 were conducted. Among the environmental categories, carbon monoxide (CO), non-methane hydrocarbon (NMHC), oxides of nitrogen (NOx), carbon dioxide (CO2) and energy consumption were selected as objectives for these optimizations. CO, NMHC, and NOx are regulated auto emissions that can have a significant impact on local and regional air quality. On the other hand,
CO2 and energy consumption are global issues influenced by CAFE standards in the US. Based on the model runs, correlations between input parameters and optimal vehicle lifetimes are discussed. In addition, policy implications of the optimization results for scrappage programs, vehicle designs, and auto emission and fuel economy regulations are discussed.
3.2 Life Cycle Optimization (LCO) Model
The Life Cycle Optimization (LCO) model is based on a dynamic programming method, and the input data to this model are determined by life cycle assessment (LCA) modeling.
3.2.1 Dynamic Programming
Dynamic programming is a collection of mathematical tools used to analyze sequential decision processes. Dynamic programming seeks the particular sequence of decisions which best satisfies a decision-maker’s criteria. In a dynamic programming model, the time horizon of the problem is the period of time over which the decisions are made. This horizon is divided into intervals, or epochs, such that one decision is made for each epoch. At each epoch, a decision is made that changes the state of the system. The state is the particular set of characteristics of the system that is analyzed over the time horizon. Using vehicle replacement as an example, a state can be defined by a vector (i,j) that represents model year i and vehicle age j. In the business sector, dynamic
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programming is typically used to help a decision-maker choose the sequence of decisions that causes the least possible cumulative cost over time (or the greatest possible reward). This best sequence, the optimal path, identifies the best decision to be made at each decision epoch over an entire time horizon. In the present study, dynamic programming is used to assess the optimal path based on environmental criteria. The decision-makers in this study include both individual car owners and policy makers.
3.2.2 Model Construction
Figure 3.1 is a schematic example of the LCO model applied to vehicle replacement. The y-axis depicts the cumulative environmental burden of a criterion (e.g.,
CO, NMHC, NOx, CO2, or energy consumption), while the x-axis represents time. For the purpose of the present study, the initial vehicle is assumed to be produced at time 0, and a new model vehicle with a different environmental profile is introduced at time Ta and Tb. Decisions to keep or replace vehicles are made at the points marked by black dots. Materials production and manufacturing environmental burdens are shown as a step function at the time a vehicle is produced. The slope of each line segment represents an energy efficiency or emission factor of a vehicle depending on the criterion to be minimized. The slopes tend to increase with time, indicating deteriorations of emission controls or energy efficiencies. Assume that, at time 0, a decision maker tries to minimize the environmental burden of a criterion within the time horizon N based on information the decision maker has regarding the environmental performance of future vehicles. The decision maker seeks a solution of the form “Buy a new vehicle at the start of year 0 and keep it for α years and retire it; then buy a new vehicle at the start of year α and keep it for β years and retire it, etc.” As an example, consider four policies depending on the decisions at Ta and
Tb. It is assumed that retiring a vehicle and buying a new vehicle occurs simultaneously.
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1. If the vehicle owner keeps the initial vehicle throughout the time horizon N,
the cumulative environmental burden (B) will result in B1. The slope change
between Tb and N represents vehicle deterioration expected for older cars.
2. If the vehicle owner replaces the initial vehicle with a new vehicle at time Ta and keeps the new vehicle until N, the cumulative environmental burden (B)
will result in B2.
3. If the vehicle owner replaces the initial vehicle with a new vehicle at time Ta
and replaces this second vehicle again at Tb, the cumulative environmental
burden (B) will result in B3.
4. If the vehicle owner replaces the initial vehicle at time Tb with a new vehicle and keeps the new vehicle until N, the cumulative environmental burden (B)
will result in B4, which is the minimum possible outcome.
With this hypothetical example, policy 4 is the optimal policy and the optimal vehicle lifetimes are Tb and N-Tb. However, in a real-world problem with a longer time horizon, the number of possible policy choices is often enormous. If a decision maker seeks an optimal replacement policy during a time horizon N with a new vehicle at the beginning of year 0, and the vehicle replacement decisions are made at the beginning of every year from year 1, the number of possible outcomes is 2N. In addition, the environmental profiles of N different model years need to be considered based on vehicle age. The LCO model provides an efficient algorithm to find an optimal policy and the dynamic life cycle inventories (LCIs) determine the environmental profiles of each vehicle model year and age. (See Section 3.2.3.)
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B3
B2
B1
B4 Environmental Burden
0 Ta Tb N Time Figure 3.1: A schematic example of the life cycle optimization (LCO) model based on four policies (B1, B2, B3, and B4 represent the final environmental burdens for the four policies.)
A mathematical model to find optimal vehicle retirement policy, as presented in Figure 3.1, is constructed using the following notation38:
n: First year of the study N: Last year of the study M: Maximum physical life of a vehicle
BM(i): Environmental burden (hereafter referred to as burden) of the materials production of model year i vehicle
BA(i): Burden of the manufacturing of model year i vehicle
38 The mathematical LCO model was developed in collaboration with the Industrial & Operations Engineering Department as a part of a project, “Life Cycle Optimization of Vehicle Replacement.”
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BU(i,j): Burden of the vehicle use during year j of model year i vehicle’s service
BR(i,j):Burden of the maintenance during year j of model year i vehicle’s service
BE(i,j): Burden of the end-of-life stage of model year i vehicle retired at the end of year j u(i,j): Burden of purchasing (producing) a new vehicle at the start of year i and keeping it for j years. For any model year i, u(i,0)=0 and represents the case in which a new vehicle is not purchased in year i. Therefore,
j BM (i) + BA (i) + BE (i,i + j −1) + ∑(BU (i,k) + BR (i,k)) if j > 0 u(i, j) = k =1 0 if j = 0
xi: The decision variable representing the number of years owning vehicle of model year i.
For each criterion, this model seeks to minimize the burden from the life cycle of model years n to N by deciding how long to keep each vehicle before purchasing a new vehicle. The dynamic programming optimality equations are constructed as follows. Let f(i) be the minimum possible burden accumulated from the start of year i through the end of year N given that a purchase is made at the start of year i. Then
min {}u(i, xi ) + f (i + xi ) ∀i = n, , N x ∈{}1,2, ,M K f (i) = i K 0 ∀ i > N
A computer program using the C language was coded to implement this model using the dynamic LCI data discussed in the following section39 (Grande 2001). The features and assumptions of the LCO model can be summarized as follows.
39 This computer program was coded as a part of a project, “Life Cycle Optimization of Vehicle Replacement”, aiming at optimizing both environmental and economic objectives (Grande 2001).
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1. The present study does not discount environmental burdens, because discounting environmental burdens are both complicated and controversial in methods and assumptions.
2. The present model describes single replacement/retirement scenarios in which one vehicle is replaced by another vehicle. Applying this model to more complicated scenarios, such as replacing one vehicle with multiple vehicles, is not attempted here.
3. The present model assumes that a vehicle is driven constant mileage per year, regardless of model year or age. For example, this scenario would apply to a household vehicle used consistently for commuting, or to a federal fleet car. A federal sedan is typically driven 12,000 miles per year for 7 years until sold to others (DOE 2002). US EPA has estimated the average VMT of the US car fleet as a function of vehicle age for the MOBILE6 emission model. It ranges, for example, from 14,900 miles at age 1 to 5,700 miles at age 20 (EPA 1998; EPA 1999). (See Chapter 4.) However, changes in the vehicle mileage per year, if any, might be abrupt transitions associated with changes of owners and driving purposes. Moreover, the present model requires equivalent annual VMT between old and new model vehicles upon vehicle replacement.
3.2.3 Model Application
As indicated in the mathematical model description, the input data to the LCO model consist of a collection of single-year environmental profiles for five life cycle phases: materials production, manufacturing, use, maintenance, and end-of-life. These environmental profiles are modeled by dynamic LCIs (Chapter 2). Three basic factors that influence the environmental performances of vehicles have been defined: regulatory/socio economic factors, technology improvements, and deterioration of a vehicle. (See Figure 2.2 in Chapter 2.) For most model years, these dynamic LCI factors are related to each other. For example, technology improvements may enhance the
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durability of emission control systems such as the catalytic converter. Accordingly, this may change vehicle deterioration behavior. In addition, seven LCI parameters reflect the evolutions of these dynamic LCI factors: recycled content, materials use, VMT, energy intensity, fuel economy, emission factors, and component reliability. These parameters are determined for each model year and/or age within the range of the optimizations. The dynamic LCIs for generic US mid-sized passenger cars with internal combustion engines between model years 1985 and 2020 were analyzed in detail in Chapter 2. The LCO model was applied to the scenario of generic US mid-sized passenger cars to evaluate the optimal lifetimes and to recommend future policies based on the dynamic LCIs determined. The model years for the optimizations are set between 1985 and 2020 and the maximum physical life of a mid-sized passenger car is assumed to be
20 years. The optimizations were conducted to minimize CO, NMHC, NOx, CO2 and energy consumption criteria. Other objectives, such particulate matter (PM), methane, and air toxics are not considered, primarily due to data limitations. For instance, since the current federal emission standards do not regulate PM for cars, estimations regarding the PM profiles of past and future model year cars are highly uncertain.
3.3 Results and Discussion
3.3.1 Optimal Lifetimes for Mid-Sized Cars
Table 3.1 gives the optimization results of generic mid-sized model scenarios. The numbers in the third column represent the optimum lifetimes of cars in the order from left to right. The optimal set of lifetimes for the energy/CO2 objectives in Table 3.1 can read, for example, "Keep the model year 1985 car for 18 years and retire it at the end of 2002, then buy a model year 2003 car and keep it for another 18 years until 2020 in
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order to minimize energy/CO2 emissions when driving a mid-sized passenger car 12,000 miles per year." (In the text, ‘[ ]’ represents the set of optimal lifetimes.)
The identical results for the energy and CO2 objectives can be attributed to fossil fuel combustion, which accounts for the majority of both energy consumption and CO2 emissions for a car. It is also notable that the regulated automotive emissionsCO, NMHC, and NOx are optimized with shorter lifetimes than the optimal lifetimes for the energy/CO2 objectives. As can be seen in Table 3.1, the optimal car life becomes shorter with increasing annual VMT, especially for CO, NMHC, and NOx emissions. Most optimal lifetimes for 24,000 miles of annual VMT scenario are less than half of the optimal lifetimes for the scenario of 6,000 miles of annual VMT. This can be attributed to the growing dominance of use phase emissions and energy consumption, as well as a high deterioration rate caused by the increasing annual VMT. In other words, as the VMT increases, driving a new, lower-emitting, and efficient car becomes more important, while the additional emissions from retiring an old car and producing the new car become relatively insignificant. On the other hand, since automobile life cycle emissions are dominated by the use phase, the cumulative environmental burdens increase roughly proportionally to annual VMT. Figure 3.2 compares the environmental burdens that will accumulate during the
36-year term of optimization years (1985-2020) when adopting energy/CO2-optimum and CO-optimum replacement policies based on 12,000 miles/year VMT. Each axis represents a cumulative environmental burden, and the optimum (minimum) environmental burden for each criterion is normalized to 1. As can be seen, the energy/CO2-optimum policy [18, 18] will create far more CO, NMHC, and NOx emissionsregulated auto emission criteriathan the CO-optimum policy. The increment ranges between 51% (for NOx) and 79% (for CO). On the other hand, the CO- optimum policy [3,3,4,6,6,7,7] keeps the other four criteria of environmental burdens
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Table 3.1: Optimal vehicle lifetimes and cumulative environmental burdens for a 36-year time horizon between 1985 and 2020
Optimal Vehicle Cumulative Environmental Burdens Objective VMT (103 Lifetimes Minimized mile/year) Energy CO2 CO NMHC NOx (years) (103 GJ) (105 kg) (106 g) (105 g) (105 g) 6 18,18 1.80 1.16 1.88 3.26 2.86 Energy/ 12 18,18 3.34 2.18 4.95 6.18 6.52 CO2 24 8,10,8,10 6.45 4.25 7.62 9.67 14.2 72 6 5,5,12,14 2.02 1.29 1.44 3.16 2.49 CO 12 3,3,4,6,6,7,7 3.84 2.46 2.76 4.29 4.54 24 2,2,2,2,2,3,3,4,4,4,4,4 7.34 4.75 5.29 10.2 12.3 6 7,15,14 1.91 1.23 1.53 2.87 2.51 NMHC 12 6,6,10,14 3.53 2.29 2.96 4.07 4.47 24 4,4,6,6,8,8 6.71 4.40 6.02 8.74 21.3 6 10,12,14 1.90 1.22 1.49 2.93 2.40 NOx 12 5,5,6,6,14 3.65 2.36 2.86 4.14 4.32 24 4,4,4,4,4,4,4,4,4 6.97 4.54 5.64 9.50 11.0
comparable to their optimums increasing CO2 by 13%, energy by 15%, and NOx and NMHC by 5% from each optimum. As shown in Table 3.1, the NMHC-optimum policy [6,6,10,14] and the NOx-optimum policy [5,5,6,6,14] do not significantly inflate other environmental burdens either. These results imply that policies that shorten vehicle lifetimes such as scrappage programs may significantly reduce CO, NOx and NMHC emissions with moderate increases in CO2 emissions and energy consumption.
Energy 2.0
1.5
1.0
CO2 CO 0.5
0.0 Energy/CO2- Optimum Policy [18,18] CO-Optimum Policy [3,3,4,6,6,7,7]
NMHC NOx
Figure 3.2: Cumulative environmental burdens accrued during the 36 years of time horizon when adopting energy/CO2-optimum and CO-optimum policies with 12,000 miles of annual VMT (The cumulative optimal environmental burdens are normalized to 1 for each criterion.)
3.3.2 Determinants of Optimal Lifetime
Distribution of environmental burdens across life cycle stages – In classical cost optimization with dynamic programming, the tradeoff between fixed and marginal cost
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plays an important role. For the LCO model, the fixed burdens are created during the materials production, manufacturing, and end-of-life stages, while the marginal burdens are created during the use and maintenance stages. The USAMP study shows that, for model year 1995 with lifetime 120,000 miles, fixed environmental burdens account for
11.8% of total life cycle CO2; 3.3% of CO; 8.1% of NMHC; and 8.4% of NOx (USAMP 1999). Optimal lifetimes tend to be longer as the ratio of fixed to marginal environmental burdens becomes greater. Technology improvement with model year – Technology improvements make vehicles cleaner and more efficient. Since the 1960s, major technological innovations for emission controls, such as catalytic converters, exhaust gas re-circulation, and computer- based sensors and engine controls have been developed to meet federal certification standards (Ross et al. 1995). Currently, clean vehicle initiative programs are guiding new technologies, such as transitional low-emission vehicles (TLEVs), low-emission vehicles (LEVs), ultra-low-emission vehicles (ULEVs), and zero-emission vehicles (ZEVs). Moreover, manufacturers have been exploring new propulsion systems and fuels, such as hybrid electric vehicles and hydrogen fuel cell vehicles. Scenarios with such leapfrog technological innovations, compared with the moderate forecasts of the present study based on conventional internal combustion cars, may dramatically reduce optimal vehicle lifetimes, especially for the energy/CO2 categories (Kim et al. 2000). On the other hand, in a slowly improving technology scenario, frequent vehicle retirements may increase overall environmental burdens within a time horizon due to the new environmental burdens associated with producing new vehicles. Efficiency improvements in materials processing, vehicle assembly, vehicle maintenance and recycling across optimization years may also influence optimal vehicle lifetimes. Vehicle deterioration with age – For the present study, vehicle deterioration is described as increasing emissions per mile. While the regulated automotive emissions
(CO, NMHC, and NOx) increase with vehicle age, CO2 emissions and fuel economy are
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not likely to deteriorate. Deterioration effects may have contributed to the short optimal lifetimes for the regulated automotive emissions compared with the lifetimes for the energy/CO2 criteria. In addition, as shown in Table 3.1, the late 1990s and future model cars generally have longer optimal lifetimes than earlier models when determined by regulated automotive emissions. This can be explained by improving durability of emission controls with newer model years. Vehicle emission deterioration is categorized in two different ways: the degradation of normal-emitters and the increasing number of high-emitters associated with the malfunctioning of emission controls. In fact, studies indicate that both normal and high-emitter deterioration have decreased remarkably for the 1990s model years and the durability of emission controls will continue to improve with future models (Austin and Ross 2001; Kim and Keoleian 2001). Regulatory/socioeconomic factors – These factors are closely linked to technological improvements and vehicle deterioration behaviors in a variety of ways. Regulations including Corporate Average Fuel Economy (CAFE) and federal tailpipe emission standards have been influencing fuel economy and emission control technologies for new vehicles. Federal certification tests also regulate emission deterioration of old cars by measuring exhaust emissions at 100,000 or 120,000 miles. In addition, many states in the US are implementing Inspection and Maintenance (I/M) programs to improve used cars’ emissions. In fact, the emission factors of cars driven in I/M program areas are known to be lower than cars driven in non-I/M areas (Wenzel 2001). Thus, in terms of regulated auto emissions, the optimal lifetimes of cars in I/M areas are expected to be longer than those in non-I/M areas. Also, the VMT of individual vehicles primarily depends on the vehicle type and purpose (e.g., household vehicles and government fleet vehicles) (DOE 2002). In the US, the average annual miles per household vehicle in 1995 was 12,200 miles, based on self-reported data, and 11,800 miles, according to odometer-based data (FHWA 2001). On the other hand, cars owned by the business sector were driven an average of 22,780 miles, while government cars
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were driven an average of 12,895 miles in 2000 (DOE 2002). Thus, as can be predicted by the optimization results, optimal lifetimes for business cars may be shorter than those for household and government cars, when determined by environmental burdens.
3.3.3 Policy Implications
In the ideal vehicle design and retirement schemes, optimal lifetimes determined for different environmental criteria would be equal to each other, and the actual vehicle retirement schedule would exactly match with this single optimal lifetime. However, the optimization results for the model years 1985 to 2020 show that the optimal lifetimes vary considerably with criteria, and differ from the median real-world lifetimes12.5 and 16.9 years for model years 1980 and 1990, respectively (DOE 2002). In particular, optimal lifetimes determined by some regulated auto emission categories are unrealistically short (e.g., 2 to 4 year optimal lifetimes, in Table 3.1, when determined by CO for a generic car driven 24,000 miles annually). Overall, optimization results suggest that there exists substantial potential for improvement in current retirement practices, emission regulations and vehicle designs. The present study demonstrates that accelerating or delaying vehicle retirement can reduce different environmental burdens. Car scrappage programs, which try to retire high-emitting old vehicles using incentives, are a well-known example. The main targets of scrappage programs have been vehicles more than 10 years old in the European countries and more than 15 years old in the US. The reimbursement in a scrappage program is provided either for a scrapped car, or upon replacement of the scrapped car with a new or cleaner vehicle. In either case, scrappage programs are known to boost car sales as well (Bohn 1992; ECMT 1999). Increased car sales can cause negative impacts on the environment by requiring additional vehicle manufacturing (ECMT 1999; Wee et al. 2000). Therefore, as highlighted in the present study, it may be critical to optimize
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age, mileage, and emission factors of vehicles, based on the target emissions for a successful scrappage program. The present study uses the average emission profile of model years to determine optimal scrappage timing of a generic car. However, if the emissions of a specific car are measured properly, the present model can provide more precise optimal scrappage timing for the car. Several studies have revealed that scrappage programs pose negative impacts in terms of CO2 emissions and energy consumption associated with additional vehicle production (ECMT 1999; Wee et al. 2000). In fact, Table 3.1 and Figure 3.2 show that shortening car lifetimes increases CO2 emissions and energy consumption. The negative impacts associated with early automobile retirements seem insignificant at first, compared with the negative impacts of keeping old vehicles. However, further analysis regarding the tradeoffs between the regulated auto emissions and CO2/energy will be necessary. The optimal lifetimes for the regulated auto emission criteria for late 1980s and early 1990s model years are 3 to 6 years (equivalent to 36,000 to 72,000 miles), based on driving 12,000 miles annually. These optimums are shorter than those for energy/CO2 objectives, and shorter than most of real-world scrappage age targets. The short optimal lifetimes for the regulated auto emissions are associated primarily with the reductions achieved in new-vehicle emissions and the deterioration or failure of emission control systems. On the other hand, deterioration seems insignificant for the energy/CO2 objectives throughout the optimizations. The optimal lifetime for these objectives (18 years) is longer than those of real-world cars, based on driving 12,000 miles annually. Thus, policies should focus on the improvement of energy efficiencies in new cars, where major gains can be made.
However, the optimal lifetimes for energy/CO2 should perhaps be longer in a realistic forecast. The EIA forecast for new car fuel economy (32.5 mpg by 2020), which has been used for the optimization, is based on the assumptions that gasoline prices will
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be higher than current prices and that advanced vehicle technologies will be developed (EIA 2001). If these assumptions are optimistic, then a plausible alternative is continued constant fuel economy, consistent with CAFE standards and average car fuel economy being nearly unchanged for the last 15 years. In this scenario, where the performance does not improve, the optimal lifetimes for energy/CO2 would be even longer. As perhaps shown by the very short optimal lifetimes determined by the regulated automotive emissions (CO, NMHC, and NOx), very strong regulations and technological developments, however, can result in shorter optimal lifetimes than real-world lifetimes. In the context of this analysis, this leads to unnecessary investment in new technologies, as vehicles in the real world may last longer than is optimal if appropriate scrappage programs are not implemented. In applying these policies, environmental impacts from other life cycle phases need to be considered as well. Policies improving vehicle durability or fuel economy may result in increased emissions during vehicle production and scrapping. This is due to the complex links among the life cycle stages, dynamic LCI factors, and those dynamic LCI parameters defined in the present study. If, for example, fuel economies are improved through the use of energy-intensive materials beyond what is already analyzed in the present study, then the materials production becomes a more important factor in assessing the optimal lifetime for energy/CO2. Rather than the optimal lifetime for energy/CO2 decreasing due to fuel economy improvements, optimal lifetime actually may increase due to the energy-intensive materials.
3.4 Conclusion
In this chapter, the optimal vehicle lifetimes that minimize the energy, CO2, CO, NMHC, and NOx objectives within a time horizon were investigated from a single vehicle perspective. A life cycle optimization (LCO) model was introduced and applied
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to automobile replacement policy to determine optimal vehicle lifetime accounting for technology improvements of new models and deteriorating efficiencies of existing models. Dynamic LCIs for different vehicle models that represent materials production, manufacturing, use, maintenance, and end-of-life environmental burdens are required as inputs to the LCO model. As a demonstration, the LCO model was applied to mid-sized passenger car models between 1985 and 2020. An optimization was conducted to minimize cumulative carbon monoxide (CO), non-methane hydrocarbon (NMHC), oxides of nitrogen (NOx), carbon dioxide (CO2), and energy use, over the time horizon (1985-2020). For CO, NMHC, and NOx pollutants with 12,000 miles of annual mileage, automobile lifetimes ranging from 3 to 6 years are optimal for the 1980s and early 1990s model years while the optimal lifetimes are expected to be 7 to 14 years for model year 2000s and beyond. On the other hand, a lifetime of 18 years minimizes cumulative energy and CO2 based on driving 12,000 miles annually. Based on the optimization results, policies improving durability of emission controls, retiring high-emitting vehicles, and improving fuel economies were recommended. This chapter presents the LCO model that determines the optimal lifetime for an objective (e.g., CO2 emissions) based on a one-to-one replacement scheme. Fleet conversion policies such as scrappage programs will be more beneficial if, as suggested in this model, complete life cycle environmental burdens of vehicles are considered. Most real-world policies are associated with multiple objectives with multiple stakeholders. Although this chapter presents a novel model that incorporates environmental objectives into vehicle replacement/retirement decisions, more sophisticated models that optimize multiple vehicle replacements based on multiple objectives will be the ultimate goal of further investigations. Also, if the life cycle environmental performance of the product systems is properly measured, this model can potentially be applied to other product systems, such as computers and telephones that are replaced frequently.
79 CHAPTER IV
OPTIMAL VEHICLE LIFE BASED ON FLEET OPTIMIZATION MODEL
4.1 Introduction
This chapter seeks to optimize "fleet conversion policy" based on the life cycle inventories of mid-sized internal combustion engine vehicles in the US. The optimal policy will minimize each regulated emission (CO, NOx, and NMHC) as well as greenhouse gas emission (CO2) from a fleet of vehicles maintaining the total mileage driven by the fleet. The results of the simulation will provide the threshold ages for vehicle scrapping and the number of new vehicles to replace the scrapped vehicles. In addition, multi-objective analysis of select air emissions is conducted using economic valuation methods. It is assumed, in this analysis, that only the age (mileage) of the vehicle, not the condition of its emissions control, is involved in the decision to scrap. Some scrappage programs have used inspections to identify vehicles most appropriate for scrappage. That option is not evaluated here. Therefore, the policy recommendations in this study do not require the identification between normal versus high emitters. The scope of the optimal fleet conversion models accounts for the entire life cycle of vehicles: materials production; manufacturing; use; maintenance; and end-of-life. As in the life cycle optimization model in Chapter 3, the input data for the optimal fleet
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conversion model are the dynamic LCIs of generic mid-sized cars model years between 1981 and 202040.
4.2 Fleet Characterization
The first step in determining optimal fleet conversion is to characterize the status quo of the fleet. A vehicle fleet is characterized by age and mileage accumulation rate within a vehicle category. Figures 4.1 and 4.2 show the distributions of survival fraction and annual vehicle miles traveled (VMT) for the passenger car fleet in the US. This study uses the fleet characterization prepared for the MOBILE6 emission model as the primary source. MOBILE6 is a computer model developed by the U.S. Environmental Protection Agency (EPA) to simulate emission factors. In Figure 4.1, the survival curve for the MOBILE6 is the curve-fit to 1996 vehicle registrations (EPA 1998; EPA 1999). Since this curve is the snapshot of 1996 fleet, it may represent the features of 1996 vehicle population rather than the survival profile of a generic vehicle. In fact, the populations of mid-1980s models in 1996 were greater than some early 1990s models because of the recession at that time, and the high survival rate of the first 10 years in the curve may reflect this anomaly (EPA 1998; EPA 1999). Due to the large number of variables associated with vehicle scrapping rates, such as local climate and road conditions, estimating a general, nationwide survival curve may pose significant uncertainties. This study uses two additional survival curves to examine the sensitivity of analysis: the survival rates of passenger cars used in the EMFAC2000 emission model developed by the California Air Resources Board (CARB); and the fitted automobile survival rates for model year 1990, estimated by the Oak Ridge National Laboratory (ORNL). The EMFAC2000 survival curve is estimated based on the
40 The dynamic LCIs for model years between 1981 and 1985 were determined based on the same processes as those used for later model years.
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1.2 MOBILE6 1 EMFAC2000 ORNL
0.8
0.6
0.4 Survival fraction
0.2
0 0 5 10 15 20 25 30 Age
Figure 4.1: Survival rate curves for cars used in this study
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16000
14000 Annual VMT of surviving car 12000 Annual VMT of initial car
10000
8000
6000 Annual miles (mile/year) 4000
2000
0 0 5 10 15 20 25 30 Age
Figure 4.2: Annual vehicle miles traveled (VMT) for cars developed for MOBILE6
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registration rates in consecutive calendar years for specific model years (CARB 2000). The survival curve used in the EMFAC2000 reflects, however, the vehicle scrapping characteristics for the state of California, which might be different from the national average. The ORNL survival rates are calculated based on both registration data and a scrappage model (DOE 2002). As shown in Figure 4.1, for older cars (14-30 years old), the survival rate estimates in the EMFAC2000 and ORNL curves are higher than those in the MOBILE6 curve. On the other hand, the MOBILE6 survival rates for younger cars (1-12 years old) are roughly equal to or higher than other estimates. The annual VMT of a surviving car in Figure 4.2 is a curve-fit result based on the 1995 Nationwide Personal Transportation Survey (NPTS) (FHWA 2001). This curve shows the VMT profile of a surviving car with vehicle age. Factoring the survival rate for the MOBILE6 in Figure 4.1, the annual VMT of initial car in Figure 4.2 describes the expected real mileage distribution by age.
4.3 Sources of Emissions
The pollutants for this simulation model include carbon monoxide (CO), non- methane hydrocarbon (NMHC), oxides of nitrogen (NOx), and carbon dioxide (CO2) emissions. Life cycle energy consumption correlate very closely with life cycle CO2 emissions and the simulations of these two objectives are virtually equivalent. For the optimal fleet conversion model of this chapter, the emissions are based on dynamic LCIs developed in Chapter 2.
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4.4 Fleet Optimization Modeling
4.4.1 Baseline Fleet
To determine the optimal fleet conversion policy, it is important to develop a reasonable approximation to fleet conversion behaviors in the real world. Figure 4.3 depicts the simplified car population and VMT distribution in 2000 based on the MOBILE6 fleet characterization. This simplified baseline fleet characterization involves the following assumptions:
1. For simulation purposes, it is assumed that 100 new vehicles have been produced each year between 1981 and 2000.
2. If surviving, vehicles are retired from the service after 20 years of physical life.
3. The distributions will be repeated every year without interruptions.
During the 20 years of physical life, vehicles can be retired from the service due to crashes or failures. Let C(j) denote the vehicle population of age j, then these natural retirements are characterized by average failure rate or hazard rate at a year j, h(j).
Cj()−+ Cj (1 ) hj()= Cj()
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140
120
100 Vehicle population
80
60
No. of Vehicles 40 20 0 120Age
25000
20000 Annual mileage rate (of surviving car) 15000
10000
VMT(mile/year)
5000
0 120Age
2.5E+06
2.0E+06 VMT distribution with age = (Vehicle Population) x (Annual Mileage Rate)
1.5E+06
1.0E+06
VMT(mile/year) 5.0E+05
0.0E+00 120Age
Figure 4.3: Car fleet in 2000 based on MOBILE6 fleet characterization
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Figure 4.4 depicts the average hazard rate of the vehicle retirements based on the MOBILE6 fleet characterization. This figure also shows that MOBILE6 models car population based on the combination of Weibull (between 1 and 12 years) and exponential distribution (beyond 13 years) (EPA 1999).
0.25
0.20
0.15
0.10 Average hazard rate 0.05
0.00 0 5 10 15 20 Age
Figure 4.4: Average hazard rate profile (fraction per year) of car fleet based on MOBILE6 fleet characterization
4.4.2 Fleet Optimization Scheme
As with most scrappage models, the rationale behind the fleet optimization model is replacing dirty old vehicles with clean new vehicles. Figure 4.5 describes the fleet optimization scheme. As discussed in Chapter 2, the life cycle emission of a vehicle typically increases with vehicle age (mileage) while decreasing with model year. Thus, the mileage in the circle is driven with higher life cycle emissions compared with the mileage of first-year vehicles. The optimum fleet conversion seeks to replace the high
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polluting mileage with the same amount of clean mileage while taking into account the emissions from the vehicle production and end-of-life.
VMT
Age
Figure 4.5: Relationships between new vehicle versus retired vehicle mileage for fleet optimization model
4.4.3 Mathematical Modeling
As already discussed, the sources of the emissions from the hypothetical fleet described in the previous section include the entire vehicle life cycle. In order to simplify the modeling equations, the vehicle life cycle will be organized into three life cycle phases: production, use, and end-of-life. The production phase accounts for the materials production as well as manufacturing phase, and use phase includes maintenance as well as vehicle operation. The emissions from these phases are modeled as functions of calendar year (simulation year) i and/or vehicle age j.
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The production fleet emission for calendar year i, FP(i), is determined by the vehicle population of age 1, C(i,1), and production emission per single vehicle, EP(i).
FiPP()=⋅ Ci (,1 ) Ei ()
The use phase fleet emission at year i, FU(i), is represented as the sum of emissions from the vehicles of age 1 through L, the maximum physical life. Let C(i,j) denote the vehicle population, V(j) the mileage during the jth year of vehicle life, and
EU(i,j) the use emission from the model year (i-j) vehicle. Then the use emission in calendar year i is:
L FiUU()=⋅⋅∑ CijVjEij ()()(),, j=1
The end-of-life emission, FE(i) is the sum of two componentsthe emissions from the retirements caused by accidents or failures plus the emissions from the forced retirements based on a scrappage program. To account for the rate of accidents or failures, the average hazard rate function, h(j), is incorporated into the equation.
L FiEE()=−⋅⋅−∑ Ci()()() 1, j hj E i 1, j j=1
L Ci1, j 1 h j Cij , 1 E i 1, j +−⋅−−+⋅−∑ ()() ()()()E j=1
The model problem is defined such that a decision maker with the baseline fleet distribution, C(i-1,j), at the end of year i-1 seeks the optimal fleet distribution at year i, C*(i,j), which will minimize the total fleet emission through year i keeping the total fleet VMT intact. Then, the fleet optimization model can be developed by the mathematical model:
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Minimize Fi()=++ FPU () i F () i F E () i Subject to hj()== 1 forj L Cij ,=> 0 forj L () LL ∑∑Ci()()()() -1, j⋅≤ Vj Cij , ⋅ V j jj==11 L If Cik() ,<== Ci ( -1, k ) , then∑ Cij () , 0 for all k 2,..., L -1 jk=+1
The first and second constraint forces the retirements of L+1 year old vehicles after the maximum physical life L, while the third constraint preserves the total fleet VMT unchanged. The last constraint will simplify the optimum solution by assuring that vehicles are retired from the oldest age. In other words, for example, k-year old vehicles can be retired only if the (k+1)-year old vehicles are completely retired. Once the optimum fleet distribution is determined for year i, then the optimum distribution for the year i+1, C*(i+1,j), can be obtained using the C*(i,j) as the baseline fleet and repeating the same simulation. In this way, a longer-term fleet conversion policy can be constructed if the required dataemissions profiles over extended periodare available.
4.5 Results
The mathematical problem developed in the previous section has been solved using a computer program in C language. Appendix C provides the complete program codes. To determine the optimum fleet conversion policy, simulations that minimize CO,
NMHC, NOx, CO2 emissions, and energy consumption have been conducted between the years 2000 and 2020 using the maximum physical life L=20. The result for the energy consumption criterion has been exactly equivalent for that of CO2 emission throughout the simulations of this study. Optimum fleet distributions, which are the result of
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simulations, are illustrated as age profiles. The baseline fleet is characterized by the addition of 100 new cars each year without accelerated scrapping.
4.5.1 Ideal Fleet Conversion
The first simulations investigate the ideal fleet conversion during 2001, separately for each emission. Since these simulations do not limit the annual production capacity of the US, the outcomes of the model runs might be purely mathematical solutions. Figure 4.6 compares the ideal fleet shapes and the emission savings by the fleet changes among the categories of emissions. The ideal fleet distribution in 2001 for CO2 is identical to the shape for 2000the baseline fleet distribution. This result implies that accelerated vehicle retirements will actually increase the overall life cycle CO2 emission and energy consumption. (Scrapping old vehicles will result in the extra vehicle production to meet the total VMT constraint.) In other words, the growth rate of new car fuel economythe positive force for scrappingis not large enough to justify scraping old vehicles. On the other hand, the ideal fleet shapes for 2001 for the regulated pollutants (CO, NOx, and NMHC) show extensive elimination of old vehicles and sharp rise in the number of new vehicles. The most substantial change of fleet is required, and the largest savings are expected, for CO optimizationscrapping vehicles older than 9 years and producing 379 new vehicles. This result is due, in part, to the relatively small fraction of production CO emission compared with NOx and NMHC. As discussed before (Section 3.3.2), the vehicle production emission ratio to life cycle emission for NMHC and NOx is significantly higher than that for CO (USAMP 1999). Thus, emissions from additional vehicle production are more significant for NMHC and NOx than for CO.
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400 400 350 350
300 Optimum fleet for CO2(energy) 300 Optimum fleet for CO s s 250 Emission Saving: 0 % 250 Emission Saving: 19.2 % 200 200 150 150
No. of vehicle No. of vehicle 100 100 50 50 0 0 120 Age 120Age
400 400 350 350 300 Optimum fleet for NMHC 300 Optimum fleet for NOx s s Emission Svaing: 5.7 % Emission Saving: 2.1 % 250 250 200 200 150 150
No. of vehicle No. of vehicle 100 100 50 50 0 0 120 Age 120Age
Figure 4.6: Optimal fleet distributions and emission savings at 2001 for different pollutants
4.5.2 Long-Term Fleet Conversion
This simulation investigates realistic long-term policy options by allowing annual production up to 120 vehicles. This can be implemented by adding a constraint to the model:
Ci(),1≤ 120
In fact, the car sales have been fluctuating between eight and twelve million since
1970 and the maximum growth has been limited within ±20% (Binder 2000). Figure 4.7 shows the optimal fleet conversion separately for CO, NMHC, and NOx emissions
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between 2000 and 2020. These distributions are determined by repeating the model runs 20 times using each previous optimum as baseline fleet. This analysis does not include
CO2 (energy) because, as shown in the previous section, any growth of vehicle production or scrapping will result in the increase of fleet CO2 (energy). Although different, each optimal conversion path requires a period of extensive production and scrapping followed by a normal production and retirement period. For example, by producing 120 vehicles and eliminating high emitting old vehicles for 3 years between 2000 and 2003, the NMHC emission will be minimized. The disrupted original fleet distribution will be recovered through normal production and retirement until the overproduced new vehicles are retired after the 20 years of physical life.
Optimal for CO 150 100 50
Population 0 Yr. 2000 Yr. 2001 Yr. 2005 Yr. 2010 Yr. 2020
Optimal for NMHC 150 100 50
Population 0 Yr. 2000 Yr. 2001 Yr. 2005 Yr. 2010 Yr. 2020
Optimal for NOx 150 100 50
Population 0 Yr. 2000 Yr. 2001 Yr. 2005 Yr. 2010 Yr. 2020
Figure 4.7: Vehicle age distributions for optimal fleet conversion between 2000 and 2020 (These are three separate programs, different for each pollutant.)
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4.5.3 Emission Reductions
The emission reductions are calculated comparing the emissions that will cumulate without policies, EN(i), and with the optimal long-term policies discussed in the previous section allowing 120 maximum vehicle production, EO(i).
Ei( ) − Ei( ) Relative Reduction() i=×NO 100() % EiN ()
Figure 4.8 represents the amounts of emission reductions that can be achieved through the long-term optimum fleet conversions. While the amounts of cumulative emission savings are greatest at 2020, the maximum relative reductions are found between 2005 and 2015 with 5% to 9% reductions depending on the pollutants. The reductions can be larger than these results if emissions are properly measured and high emitters are properly selected in a real scrappage program. Moreover, this study does not account for the high emitters older than 20 years. The optimal fleet conversion policies will be the most effective and apparent within the initial 15-year period. These results suggest scrappage programs are temporary policies associated with high emitters of relatively early model years.
4.5.4 Multi-Objective Analysis
This simulation attempts to investigate the fleet distribution that simultaneously optimizes multiple emission categories. This study uses a positive combination techniqueone of the most commonly used practical techniquesfor this multi- objective simulation. In a positive combination technique, the positive weight for each objective is determined based on priorities; a single objective function is defined by combining the weighted objectives; and the single objective function is optimized (Murty 1983).
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2.5E+09 10% CO 2.0E+09 8%
1.5E+09 6%
1.0E+09 4% Reduction Emission (g) 5.0E+08 2%
0.0E+00 0% 2000 2005 2010 2015 2020 Year
3.0E+08 10% NMHC 2.5E+08 8% 2.0E+08 6% 1.5E+08 4%
1.0E+08 Reduction Emission (g)
5.0E+07 2%
0.0E+00 0% 2000 2005 2010 2015 2020 Year
3.0E+08 10% NOx 2.5E+08 8% 2.0E+08 6% Cumulative, optimal 1.5E+08 scrapping policy 4% Cumulative, without
1.0E+08 Reduction
Emission (g) scrapping policy
5.0E+07 2% Relative reduction, right-hand scale 0.0E+00 0% 2000 2005 2010 2015 2020 Year
Figure 4.8: Emission reductions by the long-term optimal fleet conversion policy allowing 120 production
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One of the common ways to compare the priorities among emissions is to estimate the economic values of the emissions. The economic values of emissions have been estimated by others in variety of ways including market value method, damage estimate method, and control cost estimate method (Wang and Santini 1994; Small and Kazimi 1995). The estimated values vary by orders of magnitude. (Analysis of the methods and the estimated emission values are beyond the scope of this simulation.) This study focuses on a high and a low cost scenario for the regulated pollutants (CO, NOx, and NMHC) based on a damage estimate method. On the other hand, the economic value of CO2 is often measured as marginal abatement cost and typically increases with the size of the reduction. It also varies widely with countries depending on energy prices, energy supply structures, and the potential for renewable energy sources (Richels and Sturm 1996; Criqui et al. 1999). To investigate the optimal fleet distribution considering greenhouse gas and regulated pollutants simultaneously, simulations with a single objectivethe total economic cost of the emissionshas been conducted for calendar year 2001 for purposes of demonstration. An economic function U is defined using a weighting factor
(W)economic value per unit massfor each pollutant:
UW=⋅+ F W ⋅ F + W ⋅+ F W ⋅ F CO CO NMHC NMHC NOx NOx CO22 CO
Then the model optimizes the total economic cost of emissions. Although some uncertainties are inevitable due to the significant variations in the estimated values of emissions, the main features of the simulation include a trade-off between greenhouse gas
(CO2) and regulated pollutant (CO, NOx, and NMHC) objectives. Figure 4.9 highlights this trade-off using high and low cost scenarios of regulated pollutants. The scenarios are for different cities from a study based on a damage estimate method (EPA 1992). It should be recognized that many limitations exist with the applications of specific damage
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estimates to this study. For example, mobile (use phase vehicle tailpipe) and stationary source (vehicle production processes) emissions of the same pollutant pose different impacts because of differences in pollutant dispersion and the size of human populations exposed. The Y-axis represents optimum production corresponding to the ideal fleet conversion for 2001 without production limit, while X-axis represents CO2 economic values. The principal trend shows that the low CO2 value estimate tends to support accelerated-scrapping and extra-production policies. In other words, a high priority toward reducing regulated emissions will lead to policies for scrapping old cars.
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High Cost Scenario, CO $3/ton; NMHC $6911/ton; NOx $14483/ton (1989 dollars) Low Cost Scenario, CO $1/ton; NMHC 150 $98/ton; NOx $5559/ton (1989 dollars)
100 Optimum Production in 2001
50 0 100 200 300 400 500 CO2 Value ($/ton)
Figure 4.9: CO2 cost and the optimal production (EPA 1992)
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4.5.5 Sensitivity of Results
The optimal fleet distributions determined in this study depend heavily on the characteristics of the baseline fleet. In order to examine the sensitivity of the analysis, the EMFAC2000 and ORNL survival curves are used to describe the baseline fleet in determining the optimal fleet distributions. Table 4.1 compares the optimal production of new cars and the emission savings (%) in the ideal fleet conversion scheme based on different survival rate scenarios. The overall trends of the ideal fleet conversion remain unchanged across the survival curves; accelerated vehicle retirements will increase life cycle CO2 emissions and energy, but will reduce overall life cycle CO, NMHC, and NOx emissions. For the regulated emissions, the optimal production and emission reductions based on the EMFAC2000 and ORNL survival rates are greater than those based on the MOBILE6 survival rates, because the survival rates of older cars are higher in the EMFAC2000 and ORNL estimations. Therefore, in the latter estimations, more scrapping of high-emitting old cars and more production of cleaner new cars optimize overall fleet emissions. However, the MOBILE6 curve, which is a snapshot of a calendar year, is more appropriate for this study than the other curves based on specific model years. The differences among survival rates are relatively insignificant factors in determining a long-term fleet conversion scheme where new car production is limited to 120 cars (20% increase). For modeling purposes, the fleet optimization model introduced in this study simplifies real-world fleet conversions with several assumptions. In the real world, the mileage driven by retired vehicles will be replaced by mileage driven by more than one vehicle age cohort. Optimal new car production and emission savings for regulated emissions will be moderate if the VMT driven by multiple age cohorts replace the high- polluting VMT of retired cars. A study regarding the benefits of scrappage programs
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reveals that emission reductions from scrapping old vehicles vary depending on the redistribution of mileage across the remaining age cohorts (Deysher and Pickrell 1997).
Table 1: Optimal new car production and emission savings at 2001 based on the survival rate scenarios (It is assumed that 100 new vehicles have been produced each year between 1981 and 2000.) Optimal New Car Production for Different Pollutants Survival Rate (Emission Savings Compared with Baseline Fleet) Scenarios CO2 CO NMHC NOx 100 379 196 183 MOBILE6* (0.0%) (19.2%) (5.7%) (2.1%) 100 381 221 209 EMFAC2000 (0.0%) (22.7%) (7.6%) (2.9%) 100 445 261 261 ORNL (0.0%) (26.6%) (10.2%) (4.3%) *See Figure 4.6 for the results based on the MOBILE6 survival rate scenario.
This chapter assumes that vehicles are scrapped from the oldest age cohorts until one cohort is exhausted, although a real-world scrappage policy would instead scrap a range of adjacent age cohorts simultaneously. This assumption, however, would have only a small impact on optimal fleet conversions.
4.6 Discussion
4.6.1 Fleet Conversion Policy
Efforts to reduce automotive emissions have been implemented in both engineering and policy domains. In spite of significant progress in reducing CO, HC, and NOx from new vehicles when measured in certification tests, air qualities in some urban areas are far from satisfactory, since a large fraction of emissions originate from the malfunctioning of emission control of older vehicles as well as off-cycle driving (Ross et
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al. 1995). Scrappage programs are an attempt to reduce these emissions by recruiting and scrapping old, high-polluting vehicles with some compensation to the vehicle owners. Although different in the forms and the amount of incentives, European countries, such as France and Italy as well as various local governments in the US have implemented such policies to a limited extent. Scrappage might be a temporary need since the improved durability in emission controls will reduce the malfunctions of old vehicles. However, the incidence, in newest vehicles, of malfunctioning emission controls is uncertain. As discussed in Chapter 1, the goal of scrappage programs also includes boosting new car sales (ECMT 1999; Stoffer 2002). Surprisingly, few analyses have considered the environmental impact from additional car sales (ECMT 1999). This study takes into account the environmental burdens from the production of additional new vehicles. Scrapping cars, materials production, and manufacturing of cars are the main sources of those environmental burdens. According to the simulation results of this study, new car production will create significant CO2 emissions and consequently, scrapping vehicles younger than 20 years and producing more vehicles will result in a net increase of CO2 emissions, although it is relatively small. This is mainly a result of the failure to significantly reduce CO2 emissions with model year. In the same manner, regulated emissions associated with vehicle production increase due to accelerated vehicle scrapping. However, as demonstrated by the simulations, the net regulated emissions would be reduced by retiring vehicles at certain ages younger than 20 years old, depending on the pollutants. This contrasting result can be attributed to the frequent failures of emission controls for the early model year and, on the other hand, significantly improving emission factors of future vehicles. Caution has to be taken when applying this study for a specific scrappage scheme since this study uses average emissions for a vehicle age (mileage) and model year. The main targets of scrappage programs have been for vehicles over 10 years old for
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European countries and over 15 years old for North America (EDF and GM 1998; ECMT 1999). However, some vehicles are selected for the scrappage program after failing Inspection and Maintenance tests. The malfunctioning vehicles generally emit more than the average emissions among vehicles of the same model year and age (mileage) group. Therefore, if the emissions were properly measured, scrappage programs would be more effective in reducing regulated emissions than indicated by this study. In the simulation for long-term fleet conversion allowing for an increase in production from 100 to 120 units, as can be seen in Figure 4.7, dramatic changes in the composition of fleet are observed. It requires 15 to 20 years, depending on the emissions, to return to the original fleet distribution shape after a period of accelerated-scrapping and extra-production. According to the simulation results, a range of model years will be highly populated during this period while older vehicles are being retired. The increased size of a model year cohort may lower the prices of those model years and conversely, the reduced size of a model year cohort may raise the prices of those model years. Consequently, the rarity of old cars can raise the reimbursed price for recruiting cars to be scrapped (ECMT 1999). This might affect the economic viability of the simulated long- term fleet conversion scenario. However, this study does not pursue any economic cost and benefit analysis for a fleet conversion mainly due to the complexity and uncertainty of the market mechanisms.
4.6.2 Multi-Objective Analysis
This study highlights major differences in scrappage programs based on the greenhouse gas and local regulated emissions. The simulation shows, particularly for the regulated pollutants, the characteristics of the tradeoff are likely to depend on the estimation methods. The control cost estimates for emissions are generally larger than the damage cost estimate, sometimes an order of magnitude. The estimated values by
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these two methods are compared for some major US cities in various studies (EPA 1992; Wang and Santini 1994; Small and Kazimi 1995). The estimation method can be chosen based on the purpose of study, decision-maker's preference, and data availability. In addition, the regulated emission cost varies with geographical locations. For example, large metropolitan areas have a greater need to support scrappage programs due to the high cost of regulated emissions. On the other hand, small towns in a desert should support keeping the old vehicles to save greenhouse gases since the regulated emissions will not significantly damage air quality.
4.7 Conclusion
The aim of Chapter 4 is to optimize fleet conversion policy based on life cycle emissions of mid-sized generic vehicles. A baseline fleet at 2000 is defined in terms of vehicle population and VMT distributions based on the MOBILE6 fleet characterization. The life cycle emissions from each model year are estimated as function of vehicle mileage (age) using dynamic LCIs. The optimal fleet conversion model determines the optimum production rates and scrapping policies by comparing emissions created by the use of old, high-polluting vehicles versus scrapping and producing more vehicles. A simulation that optimizes fleet conversion between 2000 and 2001 has been conducted for CO, NOx, NMHC, and CO2 criteria. By repeating the simulations and limiting the increase of vehicle production, more realistic, long-term fleet conversion policies have been explored between 2000 and 2020. A trade-off between the fleet conversion policies minimizing regulated emissions and greenhouse gas has been found. According to the simulation results, accelerated-scrapping and extra-production policies are effective for abating regulated emissions while increasing greenhouse effects. On the other hand, keeping the current fleet distribution without scrapping is the best policy for CO2 emission. In this regard, optimal fleet conversions are dependent on the relative
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importance of each abatement strategy. The unit damage and control costs of emissions vary with various factors including the methods of estimation and geographical conditions. This implies that each scrappage program should be carefully designed based on the scale and target pollutants of the program. The major determinants for the optimal fleet conversion policies include the emission distributions among vehicle life cycle phases; the future emission scenarios associated with regulatory and technological progresses; and the emission control deterioration or failures with vehicle mileage (age). Controlling these determinants can affect the optimal fleet conversion policies. For example, a tighter fuel economy policy than the current scenarioprojections in Annual Energy Outlook 2001would support an accelerated scrappage program for CO2 pollutant, and eliminate or mitigate the trade- off between the regulated pollutants and CO2. It is because the CO2 saved by the improved fuel economy would outweigh the CO2 emissions from the extra production of new models. Caution must be exercised, however, when analyzing the effectiveness of policies using the present simulation model due to the complicated feedback between determinants. For example, this analysis holds true only when the improved fuel economy can be achieved with negligible increment of CO2 emissions from vehicle production. The successes of fleet conversion policies such as scrappage programs need to be evaluated both from environmental and economic perspectives considering entire vehicle life cycles. This study analyzes optimal scrappage programs based on LCA framework and data. On the other hand, the amount of incentives rewarded for scrapped cars and the increasing demand for new and used cars following the scrappage programs determine the economic feasibility. This leads to the conclusion that a holistic approach embracing both the LCA methodologies and economic analyses is fundamental for coordinating successful scrappage programs among vehicle owners, vehicle manufacturers, and policy-makers.
103 CHAPTER V
IMPROVING INSPECTION AND MAINTENANCE PROGRAMS USING RISK ANALYSIS OF EMISSION CONTROL SYSTEMS
5.1 Introduction
Although inspection and maintenance (I/M) programs have been used to identify and repair high-emitters, the emission reductions associated with these programs are still unclear. This is, in part, due to the limitations in measuring real-world fleet emissions. A significant fraction of the vehicles that fail I/M tests does not return to the next I/M cycle. These vehicles may be scrapped, sold or registered in other states or districts, or illegally driven by owners avoiding I/M requirements (Glover and Brzezinski 1997; Ando et al. 2000; Wenzel 2001). Although a vehicle is required to pass each and every I/M cycle, some vehicles that failed earlier I/M tests are tested again at the next I/M cycle even without passing the previous I/M tests (Wenzel 2001). In addition, the durability of I/M repairs is problematic. If vehicles fail inspections, the failed vehicles may be repaired either by the owners or by mechanics to pass the retests within the same I/M cycle41. Analyses show that the majority of those repaired vehicles eventually pass one of the following retests after part replacements, tune-ups, or other appropriate measures within the same I/M cycle (Wenzel 2001). However, even after successful repairs during the previous I/M cycles, some vehicles fail again at the next I/M cycle. Based on an analysis of the Phoenix IM240 program, 40% of
41 For example, the IM147 program in Arizona requires vehicles that failed initial tests to pass retests within 5 months. The retests after 5 months are considered as initial tests.
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the vehicles that had failed the initial 1995 I/M tests and eventually passed the tests later in 1995, again failed the initial 1997 I/M tests (Wenzel 2001; Wenzel 2001). Furthermore, half of the repeated failures across the two I/M tests were for the same combination of high-emitting pollutants. This suggests that I/M repairs are often incomplete and temporary measures aiming only at passing the subsequent retests. Thus, emission reductions achieved by I/M programs would appear to diminish between I/M cycles. However, few investigations have been conducted on the causes and types of emission failures, especially for repeating high-emitters. Eastern Research Group has investigated the durability of I/M program repairs based on the Arizona I/M repair database for calendar years 1997 through 2001. By measuring the time spans between the same component repairs since the first repair, the analysis shows increasing catalyst repair rates over time since the first repair. On the other hand, exhaust-gas recirculation (EGR) repairs and tune-up rates decreased with time after the first repair. However, these trends are difficult to explain. Many mechanics may have tried to fix high-emitters42 by replacing catalytic converters, especially for old cars. This is an easy and fast way to reduce emissions, but the problems may repeat in a short period unless the fundamental causes of failures are fixed. The use of aftermarket catalytic converters could be another reason that catalytic converter repairs increase over time. On the other hand, mechanics may ignore repairs of EGR systems which are related to NOx emissions, if catalyst repairs can achieve necessary emission reductions to pass retests (Eastern Research Group 2002) To seek effective, durable I/M repair strategies, this study investigates the fundamental causes of emission failures. Fault tree diagrams are developed to analyze the basic causes of four types of high-emitters: CO-type, NOx-type, HC/CO-type, and
42 Although there are several ways to define the ‘high-emitter’, this study uses the term ‘high-emitter’ for the cars that failed I/M emission tests.
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HC/CO/NOx-type. The Arizona IM/147 test and repair records for calendar years 2000 and 2002 are analyzed to verify the fault trees and to determine both durable and temporary repair practices. In particular, this study connects failures that repeat across I/M cycles to temporary, incomplete repairs. Other variables related to repeating high- emitters, including vehicle age and manufacturers are examined as well. Finally, effective I/M strategies are recommended based on the analysis. Additionally, a strategy combining both I/M and a scrappage program will be explored from a life cycle perspective.
5.2 Fault Tree Analysis of Emission Control Systems
Fault Tree Analysis (FTA), which is also called root-cause analysis, is a deductive process to identify causes of hazards in a system. It uses standard symbols for analyzing hazards associated with process controls, management procedures, and software (Haimes 1998; Raheja 1999). Figure 5.1 shows the major symbols used for the FTA, which were originally developed for electronic circuit design. Since the 1960s, when the FTA was first developed by Bell Telephone Laboratories to study the Minuteman missile launch control system, it has been widely used to improve the safety and reliability of complex engineering systems including nuclear reactors, automobile components, and wastewater treatment plants. For automotive applications, FTAs have been used to analyze design failures of components including the carburetor and accelerator, and to diagnose problems such as “Car cranks but will not fire” and “Hesitation upon acceleration” (Mateyka et al. 1973). In particular, Matsumoto et al. conducted a FTA for a reliability analysis of an automotive catalytic converter (Matsumoto et al. 1975). The study concluded that catalytic converter melting is caused by frequent misfires in more than two cylinders. The study also concluded that
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these misfires are caused by the failures of ignition components such as spark plugs, batteries, igniters, and starters.
Top Event: The primary undesired event.
Intermediate Event: A fault event which is developed further.
Basic Event: An event which requires no further development.
OR Gate: The OR gate is used when any input or combination of inputs causes the output to occur.
AND Gate: The AND gate is used when all inputs must occur for an output to occur.
Undeveloped Event: The diamond represents an event that need not be developed further, either because it is of low consequence or because
relevant information is not available.
Figure 5.1: Basic fault-tree symbols (Haimes 1998; Raheja 1999)
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5.2.1 Emission Control Systems
Automobile exhaust emissions are produced by the combustion of fuels. Incomplete combustion or unwanted substances during the combustion process are responsible for excessive exhaust emissions. For complete combustion, fuel vapors inside the engine require stoichiometric amount of oxygen. Since oxygen accounts for only 21% of air, a large amount of air is needed. Ideally, 14.7 kg of air are required for complete combustion of 1 kg of gasoline. The air/fuel (A/F) mixture is one of the most important factors affecting formation of excessive exhaust emissions. Figure 5.2 shows a schematic diagram of an air/fuel ratio control system. When the air/fuel mixture contains more fuel than the ideal ratio (14.7:1), the mixture is called ‘rich,’ while a ‘lean’ air/fuel mixture includes more air than the ideal. The electronic control unit (ECU) adjusts the amount of fuel injection based on signals from the oxygen sensors. The oxygen sensors detect oxygen concentrations of engine-out emissions that indicate the air/fuel ratios inside the engine.
Air Engine-out gas Exhaust gas Air-mass Engine Catalytic meter converter
Oxygen Fuel sensor Fuel system
ECU
Figure 5.2: Schematics of air/fuel (A/F) ratio control (Bosch 2000)
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Closed loop control systems with modern three-way or selective catalytic converters are known to be the most effective technology and have been adopted by most manufacturers since the early 1980s (Carley 1995; Bosch 2000). However, high-emitting vehicles often fail to control the air/fuel ratio. Figure 5.3 shows the exhaust emission trends around the ideal, stoichiometric air/fuel ratio (14.7:1). Since rich air/fuel mixtures lack oxygen to burn all the carbon in the fuel, the engine-engine out emission in a rich mixture contains excessive CO. On the other hand, although lean air/fuel mixtures can save fuel, they can increase the combustion temperatures inside engines and induce NOx formation. Both rich and lean mixtures can be associated with engine misfires, which pass excessive unburned hydrocarbons to the catalytic converter.
0.5 5
0.4 4
CO NOx 0.3 3 Volume (%) Volume (%) NOx, HC CO
0.2 2
0.1 HC 1
0 0 1.0 1.1 1.2 0.8 0.9 ← Lean Equivalence ratio, φ Rich →
Figure 5.3: Exhaust emissions based on air/fuel ratio (φ=1, if air/fuel ratio is 14.7:1.) (Stone 1999)
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5.2.2 Fault Tree Analysis (FTA) of High-Emitters
In this study, a high-emitter is defined as a car that has failed I/M tests. Although a car can fail emission tests for seven different combinations of pollutants, high-emitters are classified into four types based on the high-emitting pollutants and causes of the high emissions: CO-type, NOx-type, HC/CO-type, and HC/CO/NOx-type(Wenzel and Ross 1998). The HC/NOx-type high-emitters are often considered to be HC/CO/NOx-type due to the similarity of the causes (catalytic converter failures). The CO/NOx-type high- emitters are considered to be a hybrid of CO-type and NOx-type high-emitters. HC-type high-emitters were not analyzed due to their rare (<1% of high-emitters) occurrence. The emission control systems include a large number of electronic and mechanical parts. Moreover, emission control systems vary with model years due to different regulations and technologies. Thus, explaining all the reasons for high-emitters might not be feasible. This study analyzes high-emitters based on the major emission control parts inspected and repaired by the current I/M programs.
CO type CO type high-emitters run at an intermittent rich air/fuel ratio (below 14.4:1), and thus emit excessive tailpipe CO emissions (Carley 1995). Today’s automobiles have computerized management of exhaust emissions, as shown in Figure 5.2, and oxygen sensors produce voltage signals based on the oxygen concentration of engine-out gases. The ECU then orders the fuel system to adjust the air/fuel ratio toward the ideal ratio (14.7:1) based on the signals from the oxygen sensor. Figure 5.4 shows the fault tree diagram of the CO-type high-emitters. CO high-emitters are primarily caused by false signals from the oxygen sensor, malfunctioning of the ECU, or obstruction of fuel systems. In addition, bad catalysts can lead to an increase in CO emissions. False signals from an oxygen sensor may result from a mechanical failure of the oxygen sensor. There
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are many other ways that can cause the oxygen sensor to generate false ‘lean’ signals, and as a result, more enriched fuels are delivered to the engine. Air leaks in the intake or exhaust manifold are typical examples. Sometimes, ignition problems associated with spark plugs can cause the oxygen sensor to create false ‘lean’ signals as well. In addition, CO high-emissions are often created by fuel system deterioration. This deterioration can be repaired by regular tune-ups that adjust the ‘idle air/fuel mixture,’ ‘idle speed,’ and ‘dwell timing,’ depending on the fuel system of the car (Carley 1995).
CO high-emission (Runs rich )
False signal Catalyst from O sensor ECU 2 Fuel failure system
Ignition Air O2 Vacuum system intake Sensor leak Others
Figure 5.4: Fault tree diagram for CO high-emitters
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NOx type NOx type high-emitters result from high combustion temperatures together with lean air/fuel mixtures inside engines. Nitrogen, which comprises approximately 80% of air, is an inert gas and rarely combines with oxygen. However, if the combustion temperatures are extremely high (>2500°F), nitrogen combines with oxygen to form NOx (Carley 1995). Since oxygen is also needed to form NOx, maximum NOx emissions occur with a lean air/fuel ratio (Figure 5.3) (Stone 1999). Lean air/fuel ratios are primarily caused by failures and deteriorations in the air intake or fuel systems. (See Figure 5.5.) Misadjusted idle mixtures and vacuum leakages in the engine,
NOx high- emission
Catalyst failure
Runs lean Runs hot
Air Fuel EGR intake Others system
Figure 5.5: Fault tree diagram for NOx high-emitters
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carburetor, and intake manifold are well-known examples. In order to lower the temperatures in the engines and to prevent the formation of NOx, most recent model cars adopt ‘Exhaust Gas Recirculation’ (EGR) systems. NOx type high-emitters are also caused by failures of EGR systems, which recirculate a small portion of exhaust gas into the intake manifold to moderate the hot combustion temperature. Additionally, NOx high-emissions are caused by catalyst failures.
HC/CO type As shown in Figure 5.3, many HC/CO type high-emitters result from rich running conditions. Thus, most HC high-emitters are also CO high-emitters. However, a small number of HC/CO high-emitters are caused by engine misfires, which result in extremely high engine-out HC emissionsmore than 10 times that of normal-emitters (Wenzel and Ross 1998). Ignition failures in one or more cylinders cause fuels (HC) to pass through engines without combustion. Thus, excessive engine-out HC enters the catalytic converters, and often damages the catalytic converters through overheating. Failures in engine ignition systems are responsible for some misfires. Worn-out or burned-out spark plugs and obstructed plug wires are typical examples. Also, either extremely rich (richer than 8:1) or lean (leaner than 22:1) air/fuel mixtures can cause misfires. In addition, bad catalysts can lead to emissions increases for HC and CO.
HC/CO/NOx type HC/CO/NOx type high-emission is closely related to catalytic converter failures.
Catalytic converters convert HC and CO in the exhaust gas to CO2 and water vapor. In three-way converters, which have been used since the early 1980s, NOx gas is also broken down into N2 and O2. Malfunctioning of catalytic converters typically results from either catalyst failures or failure in supporting systems. Improper fuel/air ratio
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HC/CO high- emission
Moderately Catalyst rich Misfire failure
Extremely Extremely Ignition rich (>8:1) lean (<22:1) failure Others A/F A/F
Ignition system Electrical system
Spark Spark Plug plug wires control
Figure 5.6: Fault tree diagram for HC/CO high-emitters
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HC/CO/NOx high- emission
Catalyst failure
Improper A/F control (lean Misfire Sinter- Poison- Mask- or rich) ing ing ing
Supporting system failure
Misfire Mat Shell Others erosion cracking
Figure 5.7: Fault tree diagram for HC/CO/NOx high-emitters
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controls or misfires can be the fundamental causes of catalyst failures. Catalyst malfunctioning is often observed in cars with intermittent air/fuel ratio departures. In misfiring cars, high temperatures of raw fuels passing through the misfired cylinder often melt the catalyst completely or partially. In addition, normal deterioration of the catalyst, including sintering43, poisoning44, and masking45 reduces conversion performance. The thermal shock induced by the misfiring can also damage the supporting systems of catalytic converters. Structural damage to supporting systems, including mat and shell obstructions, can be caused in severe driving conditions as well.
5.3 Analysis of IM147 Records
In this section, the fault tree diagrams of the four types of high emitters are verified using the IM147 inspection and test records in Phoenix, Arizona area.
5.3.1 Overview of the Arizona IM147 Tests
For many years, the inspection and maintenance (I/M) programs in Phoenix have required vehicles to be tested before registration renewal. In 1995, the I/M program in Phoenix changed the test method from the annual two-speed idle test to the biennial IM240 test, which is similar to the FTP (Federal Test Procedure) in its acceleration schedule and maximum speed. The state of Arizona replaced the IM240 cycle with the IM147 cycle in January 2000 to reduce test time and cost (EPA 1999). Currently, the I/M program in this area requires vehicles of 1981 and later model years to be tested biennially with four years of exemptions for new vehicles. Thus, model year 1998 cars must be tested beginning with calendar year 2003. In addition, vehicles first registered in
43 Reduction of the catalytic surface area, after heat aging, by grain growth of washcoat (Al2O3) in which the catalysts are dispersed. 44 Deactivation of the catalyst by a chemical reaction with a poisonous exhaust gas such as SO2. 45 Deposition of rust, dust, or chemical compounds onto the catalytic surface area.
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this area must be tested if the model years and ages of the vehicles meet these conditions. Vehicles that failed initial tests are required to pass the subsequent retests within 5 months unless special waivers are granted. This study analyzes the IM147 test and repair records for the calendar years 2000 and 2002 to compare the emission profiles of cars across the two I/M test cycles. The tests and repairs conducted for light trucks, which account for about 30% of the I/M records, are excluded from this analysis46. The analytic methods used in this study could easily be applied to the I/M records of light trucks. The model years examined in this analysis range from 1981 to 1995. Model year 1996 and newer vehicles were exempt from the 2000 tests and, therefore, omitted from the analysis. Since the Phoenix IM147 program requires biennial tests, the cars analyzed in this study may represent approximately half of the cars with model years between 1981 and 1995 in this area. Most tests are classified as either ‘initial tests’ or ‘retests’ depending on the purposes of the tests. Although a small number of tests are marked as either ‘W’ (waiver) or ‘S’ (special), such tests are excluded from the analysis since these cars are not required to pass the I/M tests47. Cars that received multiple ‘initial tests’ in a year, which account for less than 5% of the cars, are also omitted from the analysis. Figure 5.8 shows the summarized I/M test records for cars. According to the combined database of the two I/M cycles, 352,147 and 284,906 cars were tested respectively, during the 2000 and 2002 I/M cycles, and 54% (=[171,414+19,743]/352,147) of cars that were tested during the 2000 I/M cycle were also tested during the 2002 I/M cycle. Cars that did not return to the 2002 I/M cycles
46 Light trucks (LDTs) are further classified into LDT1 and LDT2 based on weight, and each class is required to meet different I/M cutpoints. Since LDTs are required to meet less stringent emission standards than cars, the emission profiles are typically higher than cars (Wenzel 2001; Lindner 2003). 47During the 2000 and 2002 I/M cycles, 6,157 and 4,289 tests (less than 2%), respectively, were marked as either ‘W’ or ‘S.’
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Cars tested during 2000 I/M only Fail/pass during 2000 I/M Cars tested during Cars tested both I/Ms and failing during 2002 160990 the initial I/M only 2000 I/M tests 16660 (84.4%)
93749 19743 3083 (15.6%) 171414
Fail/no-pass Cars tested during both I/Ms during 2000 I/M and passing the initial 2000 I/M tests
Figure 5.8: Breakdown of the IM147 test records based on 2000 I/M tests
may have been retired, sold to other areas, or simply avoided the 2002 I/M cycle. Among the cars that were tested during both I/M cycles, 10.3% (=19,743/[171,414+19,743]) failed the initial 2000 I/M tests. Most (84.4%) of these initially failed cars were repaired and eventually passed subsequent retests during the 2000 I/M cycle. Some cars (15.6%), however, never passed the retests during the 2000 I/M cycle and returned to the program during the 2002 I/M cycle48. These cases can be explained several ways. First, these cars may have passed the retests during the 2001 I/M cycle. Alternatively, these cars may have obtained special waivers from I/M requirements. Finally, these cars may have been driven illegally in the I/M area.
48 Some cars were not tested again; others were tested again, but did not pass the I/M tests until the end of year 2000. The discrimination between these two occasions is not important for this study.
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Table 5.1: The numbers of cars that were tested in 2000 and 2002 initial tests Cars tested in both 2000 and Cars tested in 2000a Cars tested in 2002b Model 2002 Years Fails in Fails in Cars Fails Cars Fails Cars 2000 2002 1981 3,298 1,375 2,091 770 1,226 382 408 1982 4,117 1,565 2,573 835 1,568 446 448 1983 6,107 2,299 3,941 1,294 2,434 680 724 1984 10,523 3,371 6,762 1,958 4,149 975 1,085 1985 14,681 4,128 9,642 2,441 6,238 1,266 1,404 1986 17,863 6,008 12,250 3,615 7,685 1,946 1,946 1987 19,957 5,940 14,476 3,852 9,094 1,973 2,139 1988 22,639 5,304 17,020 4,086 10,668 1,837 2,296 1989 26,985 5,236 21,080 4,384 13,422 1,953 2,460 1990 27,438 5,218 22,681 4,919 14,401 2,062 2,731 1991 29,268 3,986 24,893 4,198 15,992 1,675 2,422 1992 29,429 2,774 26,238 3,348 16,828 1,202 1,903 1993 34,474 2,476 31,175 3,298 20,504 1,131 1,929 1994 33,171 2,230 31,865 3,049 19,856 1,056 1,663 1995 72,199 2,248 58,219 2,849 47,092 1,159 2,064 Total 352,147 54,158 284,906 44,896 191,157 19,743 25,622 a, b These cars include “Cars tested in both 2000 and 2002.”
Table 5.1 gives the number of cars tested during the 2000 and 2002 I/M cycles based on model years. More cars from newer model years were tested; but the greatest numbers of failures are found in cars from the late 1980s and early 1990s model years. In spite of high failure rates (See Figure 5.10.), the numbers of failures are small for early 1980s model years, because relatively small numbers of cars were tested for these model years. In order to evaluate the effectiveness of I/M repairs, the test and repair records of the initially-failed but eventually-passed cars (hereafter referred to as fail/pass cars)
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during the 2000 I/M cycle, will be analyzed in detail later in this chapter. Figure 5.9 shows the outcomes when the cars that failed the 2000 I/M tests were tested again during the 2002 I/M test cycle. The failure rates in the initial 2002 I/M tests for the fail/pass cars and fail/no-pass cars (during the 2000 I/M cycle) are 37.7% (=6274/16660) and 57.3% (=1766/3083)49, respectively. (See Figure 5.9.) As shown above, for the cars tested during both I/M cycles, the average failure rate in the initial 2000 I/M test was 10.3% (=19,743/[171,414+19,743]). In fact, although not shown in Figure 5.8, the average
Pass 10386
Fail 6274 Fail/pass 16660 Fail/no-pass Pass 3083 1317
1766 Fail
2000 I/M Cycle 2002 Initial Test
Figure 5.9: Results of the initial 2002 I/M tests for cars failed in the initial 2000 I/M tests
49 In fact, of the 3,083 fail/no-pass cars during 2000, 979 cars passed retests during 2001. (The other 2,014 cars did not pass any retests during 2001). For the 2,014 fail/no-pass cars that did not pass retests during 2001, the failure rate in the initial 2002 IM test was 62.6% (=1,317/2,104).
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failure rate increased to 13.4% in the initial 2002 I/M tests, probably because of deterioration over the two years (2000-2002). This suggests that the reliability of emission control systems of cars that failed previous I/M tests is considerably lower than that of those which passed previous I/M tests, even after successful repairs at the previous I/M cycle. This study proposes incomplete, temporary repairs as one of the important factors for these repeating failures across I/M cycles. Other factors, including manufacturers and mileages, are also explored later in this chapter. Failure rates are closely linked to vehicle age. Figure 5.10 shows failure rates of cars tested during both 2000 and 2002 I/M cycles. The failure rates in initial I/M tests generally decrease with vehicle model years. Some fluctuations of failure rates may be
Initial 2000 I/M test for cars tested during both cycles Initial 2002 I/M test for cars tested during both cycles 60 Initial 2002 I/M test for fail/pass cars during 2000 I/M cycle
40 Failure Rate (%) 20
0 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95
Model Years
Figure 5.10: Failure rates of cars tested during both 2000 and 2002 I/M cycles (See Tables 5.1 and 5.3 for details.)
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related to the fact that cutpoints of the IM147 tests vary with model years. (See Table 5.2.) For example, the sharp rise of failure rates for model year 1986 cars compared with model year 1985 cars could be the result of sudden cutpoint changes (2.4 to 1.6 g/mi for HC, 20 to 15 g/mi for CO, and 3.5 to 2.5 g/mi for NOx). Although some exceptions exist, the failure rates for fail/pass cars during the 2000 I/M cycle also decrease with model years in the initial 2002 tests. For old high-emitters, it may be difficult to diagnose the fundamental causes of emissions failures. Therefore, mechanics and car owners are likely to choose quick and easy, but deficient repairs to fix the problems (e.g., replacing catalytic converters to repair HC/CO type high-emitters).
Table 5.2: IM147 cutpoints for gasoline cars50 (g/mi) Model Years HC CO NOx 1981-82 3.0 25 3.5 1983-85 2.4 20 3.5 1986-89 1.6 15 2.5 1990-93 1.0 12 2.5 1994+ 0.8 12 2.0
Source: (EPA 1999)
5.3.2 Overview of I/M Repairs
In this section, the 16,660 fail/pass cars of the 2000 I/M cycles are analyzed. As shown in Figure 5.9, 37.7% (= 6,274 cars) of these cars failed the initial tests during the 2002 I/M cycle. This repeat failure rate seems extraordinary compared with the overall failure rates for the cars tested during both I/M cycles10.3% and 13.3%, respectively, in the initial 2000 and 2002 I/M tests.
50 Different sets of cutpoints apply to a small number of cars that do not use gasoline as the fuel.
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There are 7 different combinations of failures to IM147 tests based on pollutants51: HC; CO; NOx; HC/CO; CO/NOx; HC/NOx; and HC/CO/NOx. The IM147 tests can run for a full 147 seconds or can stop before the end of a 147-second cycle if the emission levels are so low or high that the pass or fail can be determined without running the test for the full 147 seconds. In contrast, the IM147 cycles can be repeated up to three times if the pass or fail is difficult to decide. In either case, the test results are normalized to average gram per mile for a single 147-second test, to be compared with the cutpoints in Table 5.2. Table 5.3 analyzes these seven types of failures in the initial 2000 I/M tests based on the outcomes of the initial 2002 I/M tests. Some cars that failed the initial 2000 I/M tests fail the initial 2002 I/M tests for completely different pollutants. The two failures across two I/M cycles may be caused by independent reasons. This study defines the failures in the initial 2002 I/M tests that include the same combination of pollutants that exceeded cutpoints, as ‘repeat-reason failures.’ As shown in the last row of the table, the majority of repeating failures in the 2002 I/M tests are repeat-reason failures. In particular, 85.8% (=33.7/39.3) of repeating 2002 failures are repeat-reason failures for CO high-emitters. This suggests that the CO- type high-emitters are most likely to repeat the I/M failures for reasons caused by rich air/fuel ratio. In other words, the durability of the repairs for the CO-type high-emitters is poor. On the other hand, only 9.0% of cars that failed by the CO/NOx combination in 2000, fail the initial 2002 tests for the same reason. This may be because CO and NOx
51 In addition to emission tests, vehicles are required to pass additional tests such as evaporation tests and tamper tests to pass overall IM147 tests. This study defines ‘fail’ and ‘pass’ based only on emission test results.
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Table 5.3: Breakdown of failed pollutants in the initial 2000 tests based on outcomes of initial 2002 tests
Failed pollutant in initial 2002 test (cars failed) h i i/k Failed Repeat h/k Repeat- Repeat- k g Repeat Total pollutant in d e f failure reason reason a b c HC/ initial 2000 HC/ CO/ HC/ failure failures HC CO NOx CO/ (=a+b+c+d failure failure test CO NOx NOx rate (%) in 2000 NOx +e+f+g) (=Σ*) rate (%) HC 5* 2 6 14* 0 8* 6* 41 26.1 33 21.0 157 CO 4 547* 157 375* 67* 20 52* 1222 39.5 1041 33.7 3091 NOx 20 216 1291* 331 160* 135* 166* 2319 33.2 1752 25.1 6982 124 HC/CO 15 312 154 678* 55 47 99* 1360 43.6 777 24.9 3120 CO/NOx 4 95 117 103 61* 13 36* 429 39.9 97 9.0 1076 HC/NOx 15 11 139 61 16 85* 51* 378 33.2 136 11.9 1139 HC/CO/NOx 14 57 116 147 40 42 109* 525 47.9 109 10.0 1095 Total 77 1240 1980 1709 399 350 519 6274 37.7 3945 23.7 16660 * Repeat-reason failure
failures are unrelated each other. Thus, the CO/NOx combination failures rarely repeat during the next I/M cycles. Note that such a classification method does not completely account for the repeating reasons of failures across the two test cycles. For example, HC/NOx combination failures in 2002 I/M tests, following initial failures in the 2000 tests for HC/CO/NOx combination, may share the same reason for the failures (catalytic converter malfunctioning) with the previous 2000 failures. But, they are not classified as repeat- reason failures in Table 5.3. However, this classification method seems useful in characterizing other types of repeating high-emitters. A very small number of HC-only failures are observed in the database of 2000 and 2002 I/M tests. This can be attributed to the engine-out emissions around the ideal air/fuel ratio. As shown in Figure 5.3, as air/fuel ratio becomes rich, CO emissions increase more sharply than HC emissions. On the other hand, on the lean side of air/fuel ratio, both CO and HC emissions are low. Therefore, it may be very difficult to observe HC-only failures in I/M tests. Table 5.4 summarizes the emission profiles of cars tested during both I/M cycles based on the outcomes during the 2000 I/M cycle. Fail/no-pass cars in 2000 showed the highest emissions in the initial 2000 tests. The emissions of these cars, however, decreased in 2002 initial I/M tests, indicating that certain repairs may have been attempted to fix the emission problems. Note that some of these cars (979 cars) actually passed the retests during the 2001 I/M cycle. For fail/pass cars in the 2000 I/M cycle, dramatic emission reductions are observed between the 2000 initial tests and retests. However, these reductions frequently disappear between the 2000 retests and the 2002 initial tests. For the passed cars at 2000 initial tests, the emission deteriorations between the two I/M cycles were moderate for HC and CO, and a slight improvement was observed for NOx. As can be predicted from Figure 5.3, NOx may not increase with air/fuel ratio departures, despite the fact that departures are expected with vehicle aging.
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Table 5.4: Average emission profiles (standard deviations) in 2000 initial tests, 2000 retests, and 2002 initial tests based on the 2000 I/M results
Results of 2000 I/M cycle Tests HC (g/mi) CO (g/mi) NOx (g/mi)
2000 initial 0.39 (0.31) 5.67 (3.26) 1.32 (0.58) Initial Pass 2002 initial 0.42 (0.57) 6.66 (11.13) 1.29 (0.76)
* 2000 initial 2.47 (1.32) 40.94 (34.05) 3.91 (1.43)
Fail/Pass 2000 retest 0.54 (0.40) 7.41 (3.95) 1.50 (0.62)
2002 initial 1.15 (1.20) 20.57 (29.72) 2.04 (1.25) 2000 initial* 2.94 (1.46) 48.66 (37.30) 4.15 (1.58) Fail/No-Pass 2002 initial 1.93 (1.54) 34.37 (40.23) 2.59 (1.56)
*The averages of failed emissions at 2000 initial tests. For example, 2.47g/mi is the average HC emissions from the cars that failed 2000 initial tests by pollutants including HC. The HC emissions from the fail/pass cars initially failed by CO/NOx are not included in the 2.47g/mi.
Figure 5.11 presents the emission factors in the initial 2000 tests for the cars that were tested both during 2000 and 2002 I/M cycles based on model years and high-emitter types. As might be predicted, the newer model year cars are cleaner than old model year cars. The HC/CO high-emitters were the most significant polluters for HC and CO emissions in calendar year 2000. Moreover, as shown in Table 5.3, more HC/CO high- emitters (3120) were observed in 2000 initial tests than either HC (157) or CO (3091) high-emitters. Although the NOx emission factors were highest for HC/CO/NOx high- emitters, NOx high-emitters were the largest sources of NOx emissions among the high- emitters analyzed, due to more frequent occurrences. (See Section 5.5.) Figure 5.12 compares the emission profiles of the fail/pass cars across the two I/M cycles based on high-emitter types. The three data points for each high-emitter type denote the emission factors measured in failed 2000 initial tests, passed 2000 retests, and 2002 initial tests, respectively. As can be seen in Figure 5.12, overall, high-emitters were repaired effectively after the initial failures in 2000 tests. The durability of the repairs
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appears, however, varying with high-emitter types. For example, HC/CO high-emitters are likely to deteriorate easily between the two I/M cycles compared with other high- emitter types.
HC
HC/CO high-emitters HC/CO/NOx high-emitters 6 Normal-emitters passing initial 2000 tests
4 Emissions (g/mi) 2
0 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95
Model Years
Figure 5.11: Emission profiles in the initial 2000 tests for the cars tested during both 2000 and 2002 I/M test cycles based on model years and high-emitter types (See Table 5.3.)
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CO 180 HC/CO high-emitters 160 CO high-emitters HC/CO/NOx high-emitters Normal-emitters passing initial 2000 tests 140
120
100
80
Emissions (g/mi) 60
40
20
0 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95
Model Years
Figure 5.11: Continued
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NOx 10 NOx high-emitters HC/CO/NOx high-emitters Normal-emitters passing initial 2000 tests 8
6
4 Emissions (g/mi)
2
0 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95
Model Years
Figure 5.11: Continued
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HC 5
4 HC/CO/NOx-type CO-type NOx-type HC/CO-type
3
2 130 Emissions (g/mi) Emissions
1
0 Time Time Time Time
Figure 5.12: Emission profiles of fail/pass cars across the 2000 and 2002 I/M cycles based on high-emitter types
CO
120
100 HC/CO/NOx-type CO-type NOx-type HC/CO-type
80
60
131 40 Emissions (g/mi)
20
0 Time Time Time Time
Figure 5.12: Continued
NOx
8
HC/CO/NOx-type CO-type NOx-type HC/CO-type 6
4 132 Emissions (g/mi) 2
0 Time Time Time Time
Figure 5.12: Continued
The fail/pass cars during the 2000 I/M cycle might have received repairs to pass the retests based on the failed pollutants and high-emitter types. Once a car fails an IM147 test, the Vehicle Inspection Report (VIR), which includes the emission test results, is issued to the vehicle owner for retests. After repairs, the vehicle owners or mechanics are required to fill out the forms in the VIR regarding the repaired components, cost of the repairs, repair facility etc. The repairs are coded as numbers. Repairs for “tampering inspection” failures and repairs for “pressure test” failures are coded between R1 and R6, and the emission repairs are coded between R7 and R32. However, the VIRs are often incomplete. Assuming that fail/pass cars are repaired, about 20% of emission repairs are not recorded and about 50% of cost and facility records are also missing. Table 5.5 analyzes the missing records based on high- emitter types. The no-repair record rates are comparable across high-emitter types and test outcomes. This means that the missing records may be the result of carelessness rather than a sign of fraud. In addition, the costs may often be negligible if the repairs are covered by warranties or if the vehicle owners fix the problems themselves. Based on this analysis, this study assumes that the repair trends in the missing records are the same as the trends in the available records.
Table 5.5: Missing data rates in the VIRs for failing vehicles during the 2000 I/M cycle No repair data rate (%) No cost data rate (%) Failed pollutants Pass at 2002 Fail at 2002 Pass at 2002 Fail at 2002 (high-emitter type) initial I/M initial I/M initial I/M initial I/M CO 20.1 21.2 47.7 49.2 NOx 19.2 21.2 44.4 48.9 HC/CO 18.2 19.7 49.3 48.6 HC/NOx 21.4 17.9 44.4 44.7 HC/CO/NOx 20.5 17.9 45.0 44.5
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The I/M repair database used for this study is based on the VIR reports. Some repairs were insufficient and the allegedly repaired cars failed at the following retests within the 2000 I/M cycle52. These insufficient repairs were omitted from the analysis. A small number of cars passed I/M retests multiple times with multiple repair records during the 2000 I/M cycle. In this case, only the first repair records were analyzed. Table 5.6 summarizes the repair activities and costs incurred during the 2000 I/M cycle. The rates have been adjusted to account for the missing repair and cost data. The HC-only failed cars are not analyzed due to the small number of records. The CO/NOx combination failed cars are also excluded from the repair analysis since this study assumes that either CO or NOx failures are independent incidences. As shown in Table 5.6, the HC/CO type high-emitters received the most part repairs. This may be because the misfire involves many parts in a car, including ignition systems, fuel injection systems, and air/fuel ratio controls. This also means that causes of HC/CO high-emitters are difficult to diagnose. The highest costs are incurred by HC/CO/NOx high-emitters.
Table 5.6: Repair parts and costs for the fail/pass cars during the 2000 I/M cycle
* * Failed Repair parts per car Repair costs per car pollutants Pass in Fail in Repeat- Pass in Fail in Repeat- (high-emitter initial initial reason fail initial initial reason fail type) 2002 2002 in 2002 2002 2002 in 2002 CO 2.8 2.8 2.3 208 183 184 NOx 2.3 2.3 2.3 239 203 202 HC/CO 3.2 3.3 3.4 260 229 221 HC/NOx 2.7 2.6 2.7 265 235 265 HC/CO/NOx 2.5 2.7 2.5 297 247 244
*Cars with incomplete VIRs are excluded from the analysis.
52 During the 2000 I/M cycle, 91,681 retests were attempted for the cars with model years between 1981
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Catalytic converters are known to be one of the most expensive parts. Although repair parts per car were nearly unchanged across the outcomes of 2002 initial tests, repair costs were somewhat higher for the cars that passed the 2002 initial tests. This suggests that the use of inexpensive aftermarket parts or home repairs may have affected the durability of repairs for those vehicles that had failed in 2000 and then again in 2002. Repair records are analyzed in detail in the next section based on high-emitter types.
5.3.3 Repairs of CO High-Emitters
Figure 5.13 shows the repaired parts per car for CO high-emitters during the 2000 I/M cycle. Some parts that rarely receive repairs are not present here. The rates are adjusted to account for the missing repair data. Tune-ups for fuel systems, including dwell timing, idle air/fuel mixture, and idle speed adjustments are very common repairs for CO high-emitters. However, as shown in Figure 5.13, many fail/pass cars that received tune-ups failed again during the 2002 I/M cycles, showing poor durability of the tune-ups. It is notable that among these cars, those that passed the initial 2002 I/M tests were more likely to have received oxygen sensor repairs than the cars that failed again. As predicted by Figure 5.4, fuel systems (carburetors), ignition systems (spark plugs and plug wires), and air intake systems, were also frequently repaired for CO-type high- emitters. Although the ECU (or ECM) is an important part of air/fuel ratio control, the repairs were infrequent, perhaps because failures were rare. CO-type high-emitters are closely related to the age and model year of cars. Figure 5.14 plots the failure rates based on model years. (See Table D1 in Appendix D for details.) Overall, the failure rates of the initial 2000 tests decrease with increasing model year and decreasing age. Once a car fails the initial 2000 tests, the repeat failure rate and repeat-reason failure rate jump up to 50% at the initial 2002 tests even after
and 1995 (perhaps after repairs), and 36,386 retests were unsuccessful.
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successful repairs during 2000 I/M cycle. Repairs of older CO high-emitters may be difficult. Further analysis shows that more tune-ups
70%
Pass in initial 2002 test 60% Repeat-reason fail in initial 2002 test
50%
40%
30% Repair Rates per Car 20%
10%
0%
PCV ECM EGR A/F Mix Catalyst Air Intake Plug Wire Idle Speed Spark Plug CarburetorO2 Sensor Dwell Timing Spark Control Vaccuum Leak Parts
Figure 5.13: Part repair rates for CO high-emitters (3,091 cars) repaired during the 2000 I/M cycle
(i.e., idle air/fuel mix, dwell timing, and idle speed)53 per car were conducted for old model years (1981-1987), thus the part repair rates for old model years (1981-1987) are slightly higher (2.9-3.1, depending on the outcome of I/M tests) than the average CO high-emitters in Table 5.6. Thus, more repairs might have been attempted for old, CO high-emitters despite the low durability of repairs.
53 This study confines the definition of tune-ups to fuel system services.
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Initial 2000 I/M tests Repeating failure of 2000 fail/pass cars 60 Repeating-reason failure of 2000 fail/pass cars
40 Failure Rate (%) 20
0 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95
Model Year Figure 5.14: Failure rates in initial 2002 I/M tests for CO-type high-emitters
5.3.4 Repairs of NOx High-Emitters
In this study, NOx high-emitters were found to be the most common types of high-emitters. Figure 5.15 presents the repair parts per car for high-emitters that failed only for NOx pollutant during the 2000 I/M cycle. As discussed in Section 5.2.2, hot and lean combustion conditions are important causes of NOx type high-emitters. The EGR system is one of the most frequently repaired parts for this type of high-emitter (Figure 5.15). Also, fuel system tune-ups (dwelling timing, idle air/fuel mix, and idle speed) and air intake repairs were commonly completed for NOx high emitters, although the durability of the repairs appears low.
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Those NOx high-emissions caused by catalytic converter failures may be an independent issue from lean and hot combustion conditions. Many catalyst repairs in Figure 5.15 may simply indicate catalytic converters with unreliable NOx reduction capabilities. In fact, repairs of both EGR systems and catalysts were found in only a small number of cases (6.3% of NOx high-emitters).
70%
Pass in initial 2002 test 60% Repeat-reason fail in initial 2002 test
50%
40%
30% Repair Rates per Car
20%
10%
0%
g e l t k re o s ta Mix ly n PCV F Plug ECM EGR ta rk Contr a ll Timin A/ rburetor C e Air I Plug Wi rk a w Idle Speed Spa C O2 Sensor D Spa Vaccuum Leak Parts
Figure 5.15 Part repair rates for NOx high-emitters (6,982 cars) repaired during the 2000 I/M cycle
Unlike CO high-emitters, the occurrence of NOx high-emitters seems unrelated to model year and age. As shown in Figure 5.16, failure rates in the initial 2000 I/M tests remain steady until the late 1980 model years, and decrease with newer model years. (See Table D2 in Appendix D for details.) This can be explained by the fact that the air/fuel ratio control is more reliable with fuel injections, which were phased in between
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the late 1980s and early 1990s, than with carburetors. However, the repeat and repeat- reason failure rates in the initial 2002 I/M tests (for the fail/pass cars) appear nearly constant, with some irregular peaks and falls. In other words, the repair durability of NOx type high-emitters is independent of model year and age. This may indicate that the repairs of air/fuel ratio controls are not durable either for cars with fuel injection systems or with carburetors. Another explanation may be that it is relatively easy to diagnose and repair NOx high-emitters compared with CO or HC/CO high-emitters. As shown in Table 5.6, the fewest part repairs were required to fix NOx high-emissions during the 2000 I/M cycle.
Initial 2000 I/M tests Repeating failure of 2000 fail/pass cars 60 Repeating-reason failure of 2000 fail/pass cars
40 Failure Rate (%) 20
0 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95
Model Years
Figure 5.16: Failure rates in initial 2002 I/M tests for NOx high-emitters
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5.3.5 Repairs of HC/CO High-Emitters
As indicated in Figure 5.6, the causes of HC/CO high-emissions include having either a rich or a lean air/fuel ratio. Thus, the basic causes of rich and lean air/fuel ratios are included in the causes of HC/CO high-emitters. Oxygen sensor, air intake, and fuel system repairs, as well as tune-ups were frequently conducted to repair HC/CO high- emissions. Since some HC/CO high-emitters are caused by misfires associated with ignition failures, repairs of ignition systems, including spark plugs, spark controls, and plug wires, were also very common (Figure 5.17). More detailed analysis reveals that older model years (1981-1987) are more likely to receive carburetor repairs and tune-ups. This may be for the same reason as discussed in CO high-emitter repairs: better control of the air/fuel ratio. In fact, as is the case for
70% Pass in initial 2002 test 60% Repeat-reason fail in initial 2002 test
50%
40%
30% Repair Rates per Car 20%
10%
0%
g e l t k re o s ta Mix ly n PCV F Plug ECM EGR ta rk Contr a ll Timin A/ rburetor C e Air I Plug Wi rk a w Idle Speed Spa C O2 Sensor D Spa Vaccuum Leak Parts
Figure 5.17: Part repair rates for HC/CO high-emitters (3,120 cars) repaired during the 2000 I/M cycle
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CO high-emitters, the durability of repairs generally decreased with car age (Figure 5.18). Thus, more repeat-reason failures are found for these cars. (See Table D3 in Appendix D for details.) It is noteworthy that many HC/CO high-emitters received catalytic converter repairs regardless of model year. Although the catalytic converters of HC/CO high- emitters may perform poorly, the low NOx emissions of these high-emitters indicate that the catalytic converters may not have been damaged significantly. It appears that many mechanics or owners have replaced catalytic converters as an easy way to reduce the HC/CO emissions. Although HC/CO high-emitters had, on average, more than 3 parts repaired (Table 5.6), the durability of the repairs varied significantly depending on repair strategies. The durability of the oxygen sensor, spark plug, and catalytic converter
Initial 2000 I/M tests Repeating failure of 2000 fail/pass cars 60 Repeating-reason failure of 2000 fail/pass cars
40 Failure Rate (%) 20
0 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95
Model Year Figure 5.18: Failure rates in initial 2002 I/M tests for HC/CO high-emitters
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repairs are compared in Figure 5.19. Each repair area is defined by a combination of the three repairs conducted during the 2000 I/M cycle. The number in each area represents the repeat failure rate in the 2002 initial test. Thus, for example, the center of Figure 5.19 where the three circles overlap illustrates the fact that 13.6% of those cars that simultaneously received a catalytic converter repair, an oxygen sensor repair, and a spark plug repair during the 2000 I/M cycle, failed again in the initial 2002 tests. Those catalytic converter repairs that were completed without oxygen sensor or spark plug repairs show the highest repeat-reason failure rates. On the other hand, repair combinations that included oxygen sensors showed much lower repeat-failure rates than other combinations. These trends are more evident for newer model year cars (92-95) than for older model year cars. Based on the FTA diagrams of this study, catalytic converter repairs tend to be temporary measures to reduce HC and CO emissions. More
Catalytic Converter Repairs
43.1% (31/72)
28.1% 30.8% (9/32) (4/13) 13.6% (3/22) 19.8% 24.5%
(24/121) 15.9% (26/106) (7/44) Oxygen Sensor Spark Plug Repairs Repairs
Figure 5.19: Repeat failure rates in the 2002 initial tests for HC/CO type fail/pass cars during the 2000 cycle based on repair parts (model year 1992-1995)
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fundamental cures for HC/CO high-emitters, as evidenced in Figure 5.19, might be the enhancement of air/fuel ratio controls and reduction of ignition failures. These trends become less clear as vehicle ages, perhaps because emission systems fail for multiple reasons. In addition, old cars are more likely to have received previous emission system repairs as a result of pre-2000 I/M cycle failures, which make it difficult to analyze failure/repair patterns. The emission profiles across I/M cycles are also explored in detail in Table 5.7 based on repair parts. Catalytic converter repairs that were made without also repairing oxygen sensors (both 1st and 5th rows of the table) show the lowest repair durability. On the other hand, repairs including oxygen sensors show 20%-50% lower emission factors than other repair combinations in the initial 2002 tests. This again shows that significant numbers of HC/CO high-emitters were fixed temporarily, resulting in unnecessary high- emissions and economic expense.
Table 5.7: Emissions profiles of HC/CO high-emitters across the 2000 and 2002 I/M cycles based on repair parts
HC (g/mi) CO (g/mi) Repair 2000 2002 2000 2002 Cost($)/car* combinations 2000 2000 initial initial initial initial retests retests tests tests tests tests Cat 2.89 0.49 1.41 60.77 6.53 26.00 292 O2 2.74 0.49 0.90 69.51 6.50 15.32 247 SP 2.77 0.63 1.35 54.73 7.77 24.35 201 Cat+O2 2.86 0.48 1.01 68.41 6.11 19.40 413 Cat+SP 2.91 0.55 1.35 62.79 6.12 29.82 351 O2+SP 2.86 0.57 1.02 61.40 6.72 16.54 292 Cat+O2+SP 2.79 0.52 1.09 60.63 6.38 19.05 394 Cat: Catalytic converter O2: Oxygen sensor SP: Spark plug *Adjusted to account for missing data
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5.3.6 Repairs of HC/CO/NOx High-Emitters
High emissions of HC/CO/NOx are a very common symptom of malfunctioning catalytic converters (Wenzel and Ross 1998). The malfunctioning of catalytic converters can result from either failures of supporting systems or failures of catalysts. In either case, as shown in Figure 5.7, air/fuel ratio controls and misfires in enginestypical causes for the HC/CO high-emittersmay be responsible for considerable catalytic converter failures. Thus, similar repair patterns as for the HC/CO high-emitters, such as fuel system tune-ups, ignition system repairs, and O2 sensor repairs were frequently conducted for the HC/CO/NOx high-emitters as well. (See Figure 5.17.) In addition, many HC/CO/NOx high-emitters might only have problems with catalytic converters, and these are unrelated to air/fuel ratios or misfires. Note that the average CO emissions for the HC/CO/NOx high-emitters are considerably lower than those for HC/CO high-emitters, indicating that HC/CO/NOx high-emitters have better air/fuel ratio controls and lower engine-out emissions than those of HC/CO high-emitters (Figure 5.11). As shown in Figure 5.20, for HC/CO/NOx high-emitters, catalytic converter repairs are by far the most frequently attempted and also the most durable of repairs. In some cases, HC/CO/NOx high-emitters can be caused by a combination several independent problems. For example, combinations of rich air/fuel ratio and EGR system failures may result in this type of high-emitter. Although not included in this analysis, cars that failed for HC/NOx combinations show similar part repair patterns to the cars that failed for HC/CO/NOx combinations.
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70%
Pass in initial 2002 test 60% Repeat-reason fail at initial 2002 test
50%
40%
30% Repair Rates per Car 20%
10%
0%
g e l t k re o s ta Mix ly n PCV F Plug ECM EGR ta rk Contr a ll Timin A/ rburetor C e Air I Plug Wi rk a w Idle Speed Spa C O2 Sensor D Spa Vaccuum Leak Parts
Figure 5.20: Part repair rates for HC/CO/NOx high-emitters (1,095 cars) repaired during the 2000 I/M cycle
The relationship between failure rate and age is the most complicated for HC/CO/NOx high-emitters. As shown in Figure 5.21, HC/CO/NOx high-emitters were rare in the initial 2000 tests. However, a large fraction of fail/pass cars during the 2000 I/M cycle failed again in 2002 initial tests, especially for older model years (1981-1986). Perhaps due to the complicated independent failure mechanism, the majority of failures in older model years did not repeat the same reason for failing (HC/CO/NOx). For the relatively newer model years (1987-1995), the repeat failure rate slowly decreased with some fluctuation. In addition, larger fractions of cars failed for the same reason as during the 2000 I/M cycle (HC/CO/NOx) for newer model years than for older model years. (See Table D4 in Appendix D for details.)
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100 Initial 2000 I/M tests Repeat failure of 2000 fail/pass cars Repeat-reason failure of 2000 fail/pass cars 80
60
40 Failure Rate (%)
20
0 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95
Model Year Figure 5.21: Failure rates in initial 2002 I/M tests for HC/CO/NOx high-emitters
5.3.7 I/M Analysis Based on Manufacturers
The reliability of emission controls, and the durability of I/M repairs may be related to the vehicle manufacturer. Figure 5.22 compares the initial failure rates and durability54 of I/M repairs based on select manufacturers. For relatively new model year cars (1992- 1995), the Japanese cars show lower failure rates (1.6%-2.0%) in the 2000 initial tests than the database average (4.4%). In addition, the Japanese fail/pass cars during the 2000 I/M cycle also failed at a lower rate (19.7%-23.1%) in the 2002 initial
54 Only ‘repeat failures’ were analyzed to compare repair durability. ‘Repeat-reason’ failures were not analyzed due to the relatively small number of cars in a manufacturer-based group.
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MY 92-95 50
40
30
20 Failure Rate (%) Rate Failure
10
0 Chevrolet Dodge Ford Honda Toyota Manufacturers
MY 81-91 50
40
30
20 Failure Rate (%) Rate Failure
10
0 Chevrolet Dodge Ford Honda Toyota Manufacturers
Fail at initial 2000 tests Repeat fail at initial 2002 tests
Figure 5.22: Failure rates and repair durability based on manufacturers
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tests than the average (26.0%), indicating superior repair durability. For relatively older model year cars (1981-1991), however, better performance at the initial 2000 tests did not guarantee better repair reliability. For instance, as can be seen in Figure 5.22, although the Toyota cars failed at a lower rate than the Dodge cars, the repeat failure rates in the 2002 initial tests were higher than the Dodge cars. On the other hand, although performance of the Dodge cars in the initial 2000 tests was the worst among the five manufacturers, the repair durability was second to Honda cars. This may be because more aftermarket parts are used for fixing old cars instead of genuine parts from the original manufacturers, leveling off the reliability gaps among cars of different manufactures. Also, for old cars, the repair durability may depend more on the skills of mechanics than on the auto manufacturers. It is also notable that the Honda cars show by far the best emission performance in initial tests and the best durability of emission repairs.
5.4 Emission Reductions through I/M Program
Several different methods exist to evaluate the effectiveness of I/M programs. Studies based on direct comparisons between initial tests and final tests (after repairs) during the Arizona IM240 program found mass emissions reductions of 12-15% HC, 14- 18% CO, and 7-8% NO (EPA 1997; Wenzel 1999). However, these benefits may increase to over 20% for HC and CO and over 10% for NOx, if fail/no-pass vehicles are repaired properly (EPA 1997). A study based on cumulative emissions across two I/M cycles revealed that the deterioration of normal-emitters and repaired high-emitters are important factors in evaluating I/M program benefits. Thus, the study estimated emission reductions of only 6% for HC, 3% for CO, and 1% for NOx emissions, between the 1995 initial IM240 test and the 1997 final IM240 test (Wenzel 2001). Chapter 5’s analysis evaluates emission reductions by comparing the emission profiles from the initial 2000
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IM147 test scores to the initial 2002 IM147 test scores, a complete cycle of a biennial I/M program.
5.4.1 Cars Tested During both 2000 and 2002 I/M Cycles
Table 5.8 summarizes the emission profiles of cars tested during both I/M cycles based on the outcomes of the 2000 I/M cycle. The first row of Table 5.8 represents average fleet emissions of normal emitters. With the exception of NOx emissions, these emission factors deteriorated across the two I/M cycles. The second row shows the emissions performance changes of fail/pass cars during the 2000 cycle. Although the repair efforts to pass retests dramatically reduced the average emissions of these cars, as discussed before, emission benefits also diminished between calendar year 2000 and 2002, showing much higher emissions than those of normal emitters. The emission factors of fail/no-pass cars in the third row of Table 5.8 actually decreased, indicating that there were some efforts to repair emission systems. The last row gives the total fleet’s (191,157 cars) average emissions change between 2000 and 2002. The averages of final 2000 include the retest results of fail/pass cars and fail/no-pass cars. Although the repairs of fail/pass cars accomplished the greatest emission reductions between initial 2000 and final 2000 emissions, these reductions were primarily lost through deterioration over time. Of the emissions, NOx were reduced the most (9.2%) between 2000 and 2002. As shown in Section 5.3.2, NOx emissions deteriorate less over time than other pollutants both for initial pass cars and fail/pass high-emitters. Actual fleet emissions can be determined by taking into account vehicle miles traveled (VMT) for those cars measured during the I/M cycles. As discussed in Chapter 4, VMTs generally decrease with vehicle age. Table 5.9 shows fleet emissions during calendar year 2000 and 2002 for the cars that were tested during both I/M cycles, based
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Table 5.8: Average emission factor profiles for cars tested during both the 2000 and 2002 I/M cycles
Results of Initial 2000 (g/mi) Final 2000 (g/mi) Initial 2002 (g/mi) 2000 I/M Cars cycle HC CO NOx HC CO NOx HC CO NOx
Initial 171,414 0.39 5.67 1.32 - - - 0.42 6.66 1.29 pass Fail/pass 16,660 1.32 24.40 2.96 0.49 6.47 1.44 0.94 15.46 1.88 150 Fail/no- 3,083 1.75 30.92 2.93 - - - 1.41 23.71 2.12 pass
Total 191,157 0.49 7.71 1.49 0.42 6.13 1.35 0.48 7.70 1.35
on the VMT profile associated with vehicle age and used in Chapter 4. (See Figure 4.2 of Chapter 4.) Note that emission reductions through repairs of the fail/pass cars were not considered in the 2000 fleet emissions. Cars that initially passed during the 2000 I/M cycle account for the majority of the emissions (70%-85%) in both 2000 and 2002. As a percentage of emissions, the total emission reductions between 2000 and 2002, taking into account VMT, are greater than the emission factor reductions in Table 5.8 (11%- 17.5% versus 0.1%-9.2%). This is likely due to the fact that annual VMT generally decreases as a car ages, and the same car tested in 2000 is two years older in 2002.
Table 5.9: Fleet emissions (based on VMT profiles with age) in calendar year 2000 and 2002 for the cars tested during both 2000 and 2002 I/M cycle Results of 2000 (ton/year) 2002(ton/year) 2000 I/M Cars cycle HC CO NOx HC CO NOx
Initial pass 171,414 618.7 9158.1 2146.0 589.8 9398.1 1889.7
Fail/pass 16,660 179.2 3282.4 418.7 112.9 1832.2 238.6 Fail/no- 3,083 42.8 746.2 75.2 30.7 509.0 49.0 pass
Total 191,157 840.7 13186.7 2639.9 733.3 11739.4 2177.4
5.4.2 All Cars Tested
In this section, emissions of all cars55 between model years 1981 and 1995 that were tested during 2000 and/or 2002 I/M cycles are analyzed. Section 5.4.1 analyzed only the 191,157 cars that were tested during both 2000 and 2002 cycles. In Section 5.4.1, cars that were tested in 2000 but were not tested in 2002 (160,990 cars) or that were tested in 2002 but not in 2000 (93,749 cars) are not analyzed. On the other hand,
55As discussed in Section 5.3.1, cars that were tested with a mark of either ‘W’ (waiver) or ‘S’ (special) in the records, and cars that received multiple ‘initial tests’ in a year are excluded from this analysis.
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this section takes into account the emissions from both the cars that left after the 2000 I/M cycle and the cars that were newly tested during the 2002 I/M cycle. (See Section 5.3.1.) Since the Phoenix I/M program is biennial, the emissions based on the IM147 test results may represent approximately half of the car fleet between model years 1981 and 1995 in this area. The number of tested cars is quite different between the 2000 (352,147 cars) and 2002 (284,906 cars) cycle. This indicates that the number of cars with model years between 1981 and 1995 is decreasing in this I/M area perhaps through retirements and migrations to other locations. However, the size of the total I/M test fleet between model years 1967 and 2002 increased 6% across the 2000 and 2002 I/M cycles (Walls 2003). Table 5.10 shows the emission factors of initial tests for all cars tested during the 2000 and 2002 I/M cycles. As shown in the last row of Table 5.10, the average fleet emission factors decreased by 11%-12% between 2000 and 2002, when taking into account all cars tested. Compared with the emission reductions calculated only for cars that tested during both cycles, these improvements seem remarkable especially for HC and CO. The discrepancy can be explained by examining those cars that did not return to the 2002 test cycles. The failure rates for all cars were 15.4% (=54,158/352,147) and 15.8% (=44,896/284,906) for the initial 2000 and 2002 tests, respectively. Note that the failure rates for those cars tested during both I/M cycles were 10.3% and 13.3% (Table 5.1), respectively, for 2000 and 2002 initial tests. Thus, cars that passed previous I/M tests were more likely to return to the next I/M cycle. Those cars that did not return to the 2002 I/M cycle might have been either sold outside the I/M area or scrapped. It is likely that new or used cars migrating into this I/M area replaced most of the sold/scrapped cars. As can be seen in Table 5.10, the emission factors of the cars that were tested during the 2000 I/M cycle and did not return to the 2002 I/M cycle were significantly higher than the cars that were newly tested during the 2002 I/M cycle. Late
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model year (1996 and later) cars, which were not included in the 2000 and 2002 I/M analyses, may have replaced the sold and scrapped cars. Unfortunately, exact data is not available, but it is likely that the newer cars were a lot cleaner than the older model years.
Table 5.10: Emission factors for all cars tested in the initial 2000 and 2002 tests Initial 2000 (g/mi) Initial 2002(g/mi) Test Cars Cars Results HC CO NOx HC CO NOx
Pass 297,989 0.42 5.95 1.36 240,010 0.35 4.96 1.20
Fail 54,158 1.67 29.85 2.96 44,896 1.55 27.12 2.51 1-cycle 160,990 0.76 11.90 1.75 93,749 0.65 9.99 1.52 only Total 352,147 0.61 9.62 1.61 284,906 0.54 8.45 1.41
Table 5.11 presents estimated fleet emissions based on the VMT profiles used in Chapter 4. It is notable that, compared with the cars tested during both I/M tests, the emissions from the cars that failed the initial 2000 and 2002 tests account for significant fractions (39%-47%) of fleet emissions, especially for HC and CO. This suggests that many cars that failed the initial 2000 tests, especially those that failed for HC and CO, did not return to the 2002 IM cycle. In fact, only 19,743 of the 54,158 cars that failed the initial 2000 tests returned to the next I/M cycle. The 34,415 cars that did not return might have been scrapped, sold to other areas, or driven illegally in the same I/M area. The decisions of individual automobile owners might have been important factors in the air quality in the I/M area. Overall, the benefits of I/M programs stem from repairs of high-emitters, better maintenance of vehicle owners, and fleet renewal (ousting high-emitters from the I/M area). Based on this analysis, replacing high-emitters with cleaner vehicles seems more significant than repairing the small number (16,660) of high-emitters that failed I/M tests.
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Table 5.11: Fleet emissions in calendar year 2000 and 2002 for all cars tested in 2000 and 2002 initial tests (ton/year)
Test 2000 (ton/year) 2002 (ton/year) Cars Cars Results HC CO NOx HC CO NOx
Pass 297,989 1133.9 16342.6 3783.4 240,010 680.9 9990.7 2455.2
154 Fail 54,158 711.4 12549.9 1321.5 44,896 519.4 8981.4 887.2
1-cycle 160,990 1004.6 15705.9 2465.0 93,749 466.9 7232.7 1165.0 only
Total 352,147 1845.3 28892.6 5104.9 284,906 1200.2 18972.1 3342.4
However, the underlying effect, encouraging car owners to better maintain their cars, may also be significant, since it may reduce the emission deterioration of those normal emitters that pass I/M tests.
5.5 High-Emitter Contributions to Fleet Emissions
As discussed in the previous section, cars that failed initial I/M tests account for a significant portion of fleet emissions. Figure 5.23 (a) shows the detail breakdowns of emissions from the initial fail cars (19,743 cars) during the calendar year 2000, ignoring repair effects. As shown in Table 5.9, the fleet emissions of these cars were determined to be 222.0 (=179.2+42.8) tons of HC, 4028.6 (=3282.4+746.2) tons of CO, and 493.9 (=418.7+75.2) tons of NOx, based on the annual VMTs developed for MOBILE6 (EPA 1998; EPA 1999). HC/CO high-emitters account for the greatest proportions of HC and CO emissions of the failed cars, while NOx high-emitters account for more than half of high-emitter NOx emissions. In addition, HC/CO high-emitters account for 11.0% of HC and 15.6% of CO emissions, while NOx high-emitters account for 9.6% of NOx emissions from the cars tested both years. (See Table 5.12.) Figure 5.23 (b) shows the composition of high-emitter emissions during the calendar year 2000, based on all cars tested in the initial 2000 tests. In Figure 5.23 (b), the HC and CO composition of HC/CO high-emitters slightly increased when considering all high-emitters tested, compared to Figure 5.23 (a) that considers only those high- emitters tested during both I/M cycles. On the other hand, the NOx proportion of NOx high-emitters slightly decreased compared to Figure 5.23 (a). Thus, it appears that HC/CO high-emitters are more likely to leave the I/M area after initial failures in I/M cycles. HC/CO high-emitters may have fundamental engine problems that eventually lead to retirement decisions or, at least, incur significant repair costs. HC/CO high- emitters become more important factors if all the cars that tested are taken into account.
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HC CO NOx
CO 13% Other HC/CO 6% 9% HC/CO HC/CO NOx CO CO 42% 51% 51% 9% 19% HC/CO/NOx Other 22% 9%
NOx NOx HC/CO Other HC/CO/NOx 17% 12% /NOx 6% 22% 12%
(a) Cars that were tested during both the 2000 and 2002 I/M cycles, and that failed the 2000 initial tests 156
HC CO NOx
CO Other 11% 6% HC/CO 11% HC/CO HC/CO NOx CO CO 46% 8% 46% 56% 17% HC/CO/NOx Other 23% 9% Other HC/CO/NOx NOx HC/CO/ 4% 14% NOx NOx 26% 10% 13%
(b) All cars that failed the 2000 initial tests
Figure 5.23: Emissions breakdown in the initial 2000 test based on high-emitter types
As shown in Table 5.12, HC/CO high-emitters account for 17.9% of HC and 24.2% of CO emissions from the entire fleet. The importance of the HC/CO high-emitters is also found for the cars tested in 2002. Therefore, both preventions and durable repairs of HC/CO high-emitters are recommended to improve the effectiveness of IM programs.
Table 5.12: HC/CO and NOx high-emitter contributions to fleet emissions
2000 (%) 2002 (%) Fleet High-emitter HC CO NOx HC CO NOx
11.0 15.6 1.7 7.0 9.2 1.1 Tested both HC/CO type cycles NOx type 4.6 3.8 9.6 1.6 1.2 2.8
All cars tested HC/CO type 17.9 24.2 2.9 21.4 27.4 3.8 during 2000 and/or 2002 NOx type 5.3 4.1 11.8 5.4 4.6 11.1
5.6 I/M versus Scrappage Policy Examinations
In this section, various I/M program scenarios are explored based on the fleet emissions in calendar year 2000 and 2002 determined in Section 5.4. Based on the repair and scrapping strategies, six scenarios are compared.
Scenario 1: No I/M program Without any I/M program, those cars that failed the 2000 initial tests (19,743 cars) might not have been repaired, but there are no records identifying what the emission factors for these high-emitters would be after two years of further deterioration. The actual emission factors for normal emitters after two years of deterioration are calculated in Table 5.8. The HC and CO emission factors increased by 0.03 g/mi and 0.99 g/mi, while NOx emission factors decreased by 0.03 g/mi. Based on these results, the HC and CO
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emission factors of the cars that failed the 2000 initial tests are assumed to increase by 0.03 g/mi and 0.99 g/mi in 2002. The NOx emission factors are assumed to neither increase nor decrease.
Cars tested in 2000 Cars tested in 2002 Total: 352,147 Total: 284,906
7,933 Fail (19,743) Fail (25,622) 11,810 Cars tested during Cars tested both 2000 17,689 during and 2002 Pass Pass both 2000 (171,414) (165,535) and 2002
153,725
Fail (34,415) Fail (19,274) Cars tested Cars tested during during only 2000 only 2002 Pass Pass (126,575) (74,475)
Figure 5.24: Breakdown of car fleets based on the initial 2000 and 2002 test results
Scenario 2: Current I/M program As discussed in the previous section, although repairs following the I/M test failures effectively reduced emissions during an I/M cycle, the durability of the repairs is uncertain. The durability of repairs is also related to the age of the failed car. As shown in Figure 5.24, of the 19,743 cars56 that had failed the initial 2000 tests and returned to
56 These cars include both the fail/pass (16,660) and fail/no-pass (3,083) cars.
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the 2002 I/M cycle, 7,933 cars failed the initial 2002 tests again. In addition, 17,689 cars failed the 2002 initial tests after passing the 2000 initial tests. The fleet emissions Scenario 2 are the results of the initial 2002 I/M tests.
Scenario 3: Ideal repairs In the ideal I/M repairs scenario, the 7,933 repeat-failing cars are assumed to pass the 2002 initial tests. For these 7,933 cars, the emission factors of each model year cars are assumed to be the same as those of 11,810 initial passing cars in the 2002 initial tests.
Scenario 4: Inspection and scrappage (I/S) In this scenario, cars that failed the initial 2000 tests (19,743 cars) are scrapped and replaced by new model year cars. The emission factors of these new model year cars are characterized by the average emission factors of 1996-2000 model year cars57 that passed 2002 initial tests.
Scenario 5: Inspection and scrappage of HC/CO high-emitters (I/S-HC/CO) This scenario assumes that only the 3,943 HC/CO high-emitters determined by the 2000 initial tests are scrapped and replaced by new model year cars. The emission factors of these new model year cars are characterized by the average emission factors of 1996-2000 model year cars that passed 2002 initial tests.
Scenario 6: Inspection and scrappage of NOx high-emitters (I/S-NOx) This scenario assumes that the 7,978 NOx high-emitters that failed the 2000 initial tests are retired and replaced by new model year cars. The emission factors of
57 Only a small number (2,744) of these model years were tested in 2002. These model years are excluded from the main analysis. (See Section 5.3.1.)
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these new model year cars are characterized by the average emission factors of 1996- 2000 model year cars that passed 2002 initial tests.
Table 5.13 presents fleet emissions in 2002 for all cars tested in the initial 2002 tests, based on the I/M scenarios of 2000. The emission reduction rates are calculated in comparison to Scenario 1, the No I/M scenario. The current I/M programs reduce a moderate amount of fleet emissions4.5% to 7.1%, depending on the pollutant. However, as discussed in Section 5.4, the low durability of I/M repairs degrades the benefits of the current I/M program, especially for HC and CO emissions. Table 5.13 shows that the ideal repairs scenario, which assumes perfect durability of repairs, can reduce more than twice the HC and CO emissions compared with the current I/M program. Scenario 3, the inspection and scrappage scenario, prevents the most HC (13.8%) and CO (16.6%) emissions. It is notable that selective scrapping of a small number (3,943) of HC/CO high emitters can reduce significant fractions of HC and CO (7.5% and 10.2%, respectively). To reduce NOx emissions, selective scrapping of NOx high- emitters seems the most effective (10.7%). (The inspection and scrappage (I/S) scenario may scrap some cars that did not fail for NOx pollutant.) Note that, without an I/M program, some percentage of high-emitting cars that disappeared after the I/M failures in 2000 would still be on the road in 2002 and would also affect fleet emissions. The fleet emissions in Scenario 1 (No I/M) may be smaller than the real world. Thus, the real emission reductions in the calendar year 2002 compared with 2000 are likely greater than those in Table 5.13. The emission reductions in Section 5.4.2 were based on comparisons between fleet emissions in 2000 and fleet emissions in 2002. The emission reductions in this section are based on comparisons between 2002 fleet emissions using the various scenarios. The No-I/M scenario already takes into account emission deteriorations
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between 2000 and 2002. Therefore, as shown in Scenario 2 of Table 5.13, the emission reduction percentages calculated in this section may differ from those in Section 5.4.2. The emission reductions for the scrappage scenarios in Table 5.13 do not take into account other life cycle stages. In Chapter 4, it was assumed that brand new cars replace the scrapped cars during scrappage programs. Thus, scrapping old cars and manufacturing new cars generates additional emissions. If emissions from scrapping old cars and manufacturing new cars were considered, the emission reductions in Table 5.13 would diminish considerably.
Table 5.13: Fleet emissions (reductions) in 2002 based on I/M scenarios in 2000 (ton)
Scenarios HC CO NOx
1. No I/M 1261.8 20422.7 3501.1 2. Current I/M 1200.2 (4.9%) 18972.1 (7.1%) 3342.4 (4.5%) 3. Ideal repairs 1131.4 (10.3%) 17574.8 (13.9%) 3277.6 (6.4%) 4. I/S 1087.9 (13.8%) 17035.8 (16.6%) 3149.8 (10.0%) 5. I/S–HC/CO 1167.6 (7.5%) 18336.8 (10.2%) 3312.8 (5.4%) 6. I/S–NOx 1174.7 (6.9%) 18642.9 (8.7%) 3127.9 (10.7%)
5.7 Scrappage Policies from a Life Cycle Perspective
This section explores fleet conversion policies from a life cycle perspective based on the baseline fleet in Chapter 4. In addition to the use phase emission factors, emissions from car manufacturing, maintenance, and end-of-life stages are considered in assessing the benefits of scrappage programs. To describe the life cycle emissions of a fleet (NMHC, CO, and NOx), the IM147 test records of the Phoenix area are combined with the age distributions of cars and annual mileage developed for MOBILE6. In Chapter 2, the emission factor E is modeled by the sum of emissions from FTP normal
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emitter EN, plus three the incremental emissions FTP high emitter EH, non-FTP or off- cycle driving Eoff, and non-FTP evaporation Eevap.
EE=++NHoffevap E E + E
In this section, the emissions of high-emitter and normal-emitters are determined separately. The average emission factors of each model year determined by the Phoenix I/M tests are used as a surrogate for the emissions from FTP type tests both from high
58 (EH) and normal (EN) emitters . Thus, the emission factors used for this section are described by the following equations.
EE=+ E + E (for high-emitters) Hoffevap EE=+Noffevap E + E (for normal-emitters)
Figure 5.25 presents both the emission factors measured by the IM147 cycles in the initial 2000 I/M tests, and those constructed in Chapter 2 to represent FTP-normal emissions in the calendar year 2000. The FTP type emissions in Figure 5.25 are estimated primarily based on the EPA's long-term in-use emissions survey (Austin and Ross 2001). As can be seen in Figure 5.25, those data used for the fleet conversion policy (‘FTP-normal’ in Figure 5.25) overestimate emission factors for older model years. With the exception of NOx, for newer model years, the emissions based on the two cycles, FTP and IM147, agree relatively well with each other. Of the pollutants, the CO scores by the IM147 cycles are the most consistent with the EPA’s long-term in-use emissions survey. Note that due to the low cutpoints applied to newer model year cars, some high-emitters with newer model years show lower emission factors than some normal-emitters with older model years. For example, the model years 1994 and 1995
58 The IM240 cycle is a shorthand test schedule of the FTP test, and the IM147 test cycle was developed based on the last 147 seconds of the IM240 cycle (Sierra Research 1998).
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HC 4 FTP-normal IM147-normal IM147-high
3
2 Emissions (g/mi) Emissions
1
0 1980 1982 1984 1986 1988 1990 1992 1994 1996 Model Years
CO
FTP-normal IM147-normal 60 IM147-high
40 Emissions (g/mi) 20
0 1980 1982 1984 1986 1988 1990 1992 1994 1996 Model Years
Figure 5.25: Emission factors for the calendar year 2000 measured by the IM147 cycles and those constructed based on the EPA’s in-use emission survey
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NOx 4 FTP-normal IM147-normal IM147-high
3
2 Emissions (g/mi)
1
0 1980 1982 1984 1986 1988 1990 1992 1994 1996 Model Years
Figure 5.25: Continued high-emitters both emit 0.86g/mi of HC, compared with 1.05 and 1.01 g/mi of HC from 1981 and 1982 normal-emitters. Scrappage policies that retire I/M failed high-emitters during the initial 2000 tests were examined, and the fleet emission reductions in 2001 were determined by comparing them to the baseline fleet conversion policy without scrappage programs. In this model, as shown in Figure 5.26, the VMTs driven by the new 2001 models replace the VMTs of retired high-emitters. The difference between this scheme and that of Chapter 4 is that this scheme scraps only high-emitters that failed I/M tests, regardless of vehicle age. Due to the fact that some high-emitters with newer model years are cleaner than some normal- emitters with older model years, an optimization of high-emitter scrappage was not attempted. Other assumptions and characteristics of the model for this section are summarized below.
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High-emitter Normal-emitter VMT of MY 2001 cars VMT VMT of scrapped high-emitters
Age
Figure 5.26: Relationships between new cars’ mileage versus retired high-emitters’ mileage
59 1. Other components of use emissions, including off-cycle emissions (Eoff) and 60 evaporative emissions (Eevap) are assumed to be the same both for normal- and high-emitters. This may slightly underestimate real-world high-emissions because it is likely that high-emitters emit more off-cycle emissions than normal-emitters in high-power driving conditions.
2. Fuel economies of both high-emitters and normal-emitters are assumed to be the same, although high-emitters typically consume more fuel per mile than
59 The off-cycle emissions for CO, HC, and NOx between model years 1981 and 1995 are estimated to be, 2.8, 0.05, and 0.24 g/mi, respectively. (See Section 2.4.6.2.3) 60 The evaporative HC emissions are modeled to be 1 g/mi for model years between 1981 and 1988; 0.5 g/mi for model year 1993; and 0.46 g/mi for model year 1995. The evaporative emissions are assumed to be linearly decreasing between model years. (See Section 2.4.6.2.4)
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normal-emitters. Therefore, precombustion emissions from both high- and normal-emitters are also assumed to be identical per mile.
3. Environmental burdens of high-emitters from manufacturing, maintenance, and end-of-life are assumed to be the same as normal-emitters. Although maintenance burdens of high-emitters might be higher than normal emitters, the discrepancy seems negligible considering that emissions from the use phase dominate. (See Chapter 2.)
4. The 2001 IM147 emission factors are assumed to be the average of the 2000 and 2002 initial IM147 test results.
5. The high-emitter occurrence rates are modeled by the failure rates in the initial 2000 I/M tests.
6. Since the number of IM147 records for 1996 and later model year cars are small, the normal-emitter estimations based on the EPA's long-term in-use emissions survey were used for normal-emitters with 1996 and later model years. High-emitters that came from 1996 and later model years were ignored due to their rare occurrence.
Figure 5.27 shows the fleet distribution changes between 2000 and 2001 when all high-emitters are scrapped. The baseline is the manufacturing 100 cars per year. To compensate for the VMT losses driven by the scrapped cars, 83 cars are manufactured. If HC/CO and NOx high-emitters are scrapped completely, 21 and 29 new manufacturing units would be needed respectively, to offset the VMT losses of these scrapped cars. As shown in Table 5.14, the emission reductions of scrappage programs are negligible from a life cycle perspective, especially for NMHC and NOx. In particular, the total life cycle NMHC emissions increase with the complete scrapping of high- emitters. This implies that NMHC emissions from other life cycle phases, including car manufacturing, maintenance, and end-of-life, are important factors. However, as discussed in Chapter 4, the environmental burdens from these life cycle stages may
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2000 2001 200 200 180 180 160 High-emitter Normal-emitter 160 140 140 Normal-emitter 120 120 100
Cars 100 80 Cars
167 80 60 60 40 40 20 20 0 0 120 Age 120Age
Figure 5.27: Fleet distributions in 2000 and 2001 if all high-emitters are scrapped
Table 5.14: Life cycle emission reductions and additional car manufacturing requirements to offset VMT losses from scrappage scenarios Additional Scrappage Emission reductions (%) cars scenarios 61 NMHC CO NOx manufactured* All high- -1.6 16.0 2.4 83 emitters HC/CO high- 1.3 10.4 -1.0 21 emitters NOx high- -1.9 -0.2 2.2 29 emitters *Based on 100 cars per year manufacturing as a baseline
affect areas remote from the I/M program areas. In particular, NMHC, CO, and NOx emissions may not be significant issues in non-I/M areas. Thus, from a local perspective, the benefits of scrappage programs can still be significant in many urban areas, despite the estimates in Table 5.14. Other caveats are summarized as follows.
1. In the real world, the additional car manufacturing determined by the current model would not occur in a single year, since the lost VMTs of scrappage programs can be compensated for by public transportation or by used cars.
2. The current model may have underestimated the off-cycle emissions of high- emitters by assuming the same amount of grams per mile emissions as normal emitters. Also, as discussed in the previous section, precombustion emissions and maintenance emissions might have been underestimated for high-emitters. Unfortunately, detailed estimates are unavailable for these factors. The benefits of scrapping high-emitters will increase if these factors are taken into account.
61 A conversion factor of 3.4/4.1, the ratio between the NMHC and HC for the Tier 0 emissions regulation, was used to estimate the NMHC from HC values.
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3. The overall emission performances of both normal- and high-emitters in I/M areas are likely better than those in non-I/M areas, due to better maintenance and repairs. If a scrappage program is conducted in a non-I/M area, the life cycle emission benefits are likely to be much larger than determined in this section.
4. Table 5.14 shows the benefits of scrappage programs for calendar year 2001 only. Using a longer-term perspective, as analyzed in Section 4.5.3, the benefits of a scrappage program would increase considerably if the scrappage program is repeated.
5.8 Conclusion
In this chapter, the fundamental causes of high-emitters were identified using the Fault Tree Analysis (FTA) tool. High-emitters that failed the I/M tests were classified as CO type, NOx type, HC/CO type, and HC/CO/NOx (HC/NOx) types based on combinations of excessive emissions. Fault tree diagrams were developed for each high- emitter type to define the basic causes of a high-emitter. The IM147 test and repair records in the Phoenix area were analyzed to verify the fault tree diagrams developed for each high-emitter type. Repair records of the IM147 programs were also analyzed to examine the repair durability of the emission systems. Further analysis was conducted to examine the emission reduction strategies for the IM147 program in conjunction with scrappage programs. High-emitters are caused by variety of reasons. Disruptions of air/fuel ratio controls are important causes of CO, HC/CO, and HC/CO/NOx (HC/NOx) high-emitters. A higher percentage of tune-ups and fuel system repairs were observed for relatively old cars, indicating poor air/fuel ratios. Older high-emitters of CO, HC/CO, and HC/CO/NOx types fail I/M tests more frequently than newer cars, and the durability of repairs is also lower than that of newer model years. On the other hand, the failure rates
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and repair durability of NOx high-emitters are relatively independent of age. This may be because of relatively stable NOx engine-out emissions with air/fuel ratio fluctuations. In fact, EGR or catalytic converter failures, which are independent from air/fuel ratio controls, are the most frequent causes of NOx high-emissions. Emission factor reductions based on the cars tested during both 2000 and 2002 cycles were relatively small, especially for HC and CO (<2%). For total fleet emissions that take into account annual mileage profiles, more reductions (11%-18%) were observed due to decreased VMT with increasing vehicle age. If all cars that were tested during the 2000 and/or 2002 cycles are considered, the emission factor reductions become more significant, especially for HC and CO (11%-12%). Since many cars with model years between 1981 and 1995 left the I/M area after the 2000 I/M cycle, the total fleet emission reductions are even greater (34-35%) based on all cars that were tested in each I/M cycle (Table 5.11). Clearly, many cars that failed I/M tests do not return to the next I/M cycle, and this ‘fleet renewal’ affects overall emissions. Among the high-emitters, HC/CO high-emitters are the greatest sources of HC and CO emissions, while NOx high-emitters are the most important NOx emitters. In particular, the importance of HC/CO high-emitters is more evident if all cars that were tested during each cycle are considered. This means that HC/CO high-emitters are most likely to disappear from the I/M area after failing I/M tests. Scrapping high-emitters after inspections can reduce more emissions than I/M programs if newer model year cars with better emission performances replace the scrapped cars. From a life cycle perspective, however, the environmental burdens from the scrapping of high-emitters and additional manufacturing of new cars are also important factor to determine overall benefits. Based on this analysis, I/M programs are beneficial. Repairs of high-emitters and better maintenance of normal-emitters contribute to air quality improvements. The
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greatest benefit, however, may come from the encouragement to owners to stop driving high-emitters in the I/M areas.
171 CHAPTER VI
CONCLUSION
6.1 Research Scope and Key Findings
The automobile, a critical transportation mode worldwide, provides essential mobility for modern society and facilitates economic and social activities. The continuing growth of demand for automobiles, however, threatens human health and ecological values on both local and global scales through a variety of pollutants and mechanisms. Cars and light trucks contribute to poor urban air quality and greenhouse gas emissions, due in large part to their reliance on fossil fuels. Since the 1960s, automobiles in the US have been subject to emission standards for HC, CO, and NOx. These regulatory standards have guided innovations in emission control technologies, such as exhaust gas recirculation systems, catalytic converters, and computerized engine controls (Ross et al. 1995). Many urban areas in the US, however, still suffer from chronic air quality problems. Energy consumption from cars and light trucks use also poses significant challenges for addressing global warming and energy security. Although CAFE standards affect the fuel economies of new model cars and light trucks, they have been unchanged since the mid-1980s. With increasing vehicle miles traveled, energy consumption by cars and light trucks is increasing every year in the US (DOE 2002). Pitfalls in the recent progress in the transportation sector include problems with the continuing use of old high-emitting vehicles. A small number of high-emitting vehicles account for significant fractions of ozone precursors and toxic gases. Although
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cleaner, more efficient new vehicles are currently available, vehicle owners continue to drive old vehicles primarily for economic reasons. On the other hand, LCA studies reveal that environmental burdens are also created by retiring old vehicles and manufacturing new vehicles. For example, an LCA study shows that the complete manufacturing process for a mid-sized generic passenger car consumes 126 GJ of primary energy13% of life cycle energy based on 120,000 miles of functional unit (Sullivan et al. 1999; USAMP 1999). From an environmental perspective, a sustainable fleet conversion policy should remove inefficient, old technologies and adopt efficient, new technologies. At the same time, a fleet conversion policy should consider environmental burdens from the perspective of the complete life cycle of a vehicle. In the US, scrappage programs and the inspection and maintenance (I/M) programs are the two main policies currently implemented to reduce emissions from old, high-emitting vehicles. Implemented by local governments, the primary goal of these programs is to improve the local air quality in non-attainment areas (defined by the US EPA). Scrappage programs recruit and scrap old, high-emitting vehicles with certain economic reimbursements to vehicle owners. I/M programs require mandatory vehicle inspections and, if necessary, appropriate repairs before registering vehicles in non- attainment areas. This study devoted Chapters 3 and 4 to assessing scrappage programs from a life cycle environmental perspective, while Chapter 5 evaluates the issues and benefits of I/M programs, largely from the perspective of a locality. Chapter 3 of this study explored the optimal lifetimes of mid-sized generic cars based on a 36-year time horizon (between calendar year 1985 and 2020). For CO, NMHC, and NOx pollutants with 12,000 miles of annual mileage, automobile lifetimes ranging from 3 to 6 years are optimal for 1980s and early 1990s model years, while optimal lifetimes are expected to be 7 to 14 years for model year 2000s and beyond. On the other hand, a lifetime of 18 years minimizes cumulative life cycle energy and CO2 based on driving 12,000 miles annually. The median lifetime for a 1980 model year car
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was 12.5 years, and that for a 1990 model year car is expected to be 16.9 years. Thus, generally, cars are driven for a longer time than optimal period from a regulated auto emissions perspective, while median automotive lifetimes have been almost ideal from a
CO2 and energy perspective. This suggests that accelerated vehicle scrappage programs are likely to reduce life cycle HC, CO, and NOx emissions, but may increase life cycle
CO2 and energy. The optimal lifetimes are closely related to distribution of environmental burdens across life cycle stages; technology improvement with model year; vehicle deterioration with age; and regulatory/social factors. In addition, Chapter 4 of this study investigated both ideal and practical fleet conversion policies separately for three regulated pollutants (CO, NMHC, and NOx) and
CO2 objectives. According to the simulation results, accelerated scrapping policies are generally recommended to reduce regulated emissions, but they can increase greenhouse gases. The single vehicle replacement model in Chapter 3 may be useful for an individual vehicle owner who drives a vehicle on a regular basis for a certain purpose. On the other hand, the optimal fleet conversion model in Chapter 4 is more suitable for a policy maker or fleet manager who decides to scrap vehicles based on vehicle emission performance. The results of the model runs, however, are consistent across the two modelsshort optimal lifetimes for regulated auto emissions (CO, NMHC, and NOx) and long optimal lifetimes for energy/CO2. Chapter 5 analyzed the issues and benefits of I/M programs based on the IM147 test and repair records in Phoenix, Arizona area. The fault trees analyses, in conjunction with the repair analyses, show that repairing high-emitting vehicles is often difficult and left incomplete. For instance, among the cars that had failed the initial 2000 I/M tests, 37.0% of cars failed the initial 2002 I/M tests even after successful repairs. This trend can be partially explained by the fact that many mechanics or vehicle owners choose easy but temporary repairs, such as catalytic converter replacement, to pass the consecutive
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retests after an initial failure in the I/M tests. The analyses in Chapter 5 also conclude that I/M programs are beneficial, but improved inspection programs and enhanced repair strategies will be able to reduce a higher percentage of emissions than current I/M programs. Although it is difficult to measure, the indirect effects of I/M programsexpelling high-emitters from an I/M area and encouraging vehicle owners to better maintain their carsmay be significant benefits for the I/M locality.
6.2 Policy Implications
Life extension is an important, well-recognized green engineering strategy, which can be achieved by enhancing product durability or enabling reuse/recycle options (EPA 1993; Stahel 1994). On the other hand, replacement of older, inefficient product with newer, more efficient products is another important mechanism for reducing environmental impacts. Although product life extension limits manufacturing-related environmental impacts, adopting newer technology can enhance environmental performance during the product use life cycle stage. As analyzed in Chapter 2, the use phase is the most important source of environmental burdens during the life cycle of a vehicle. Therefore, an optimal retirement policy should take into account environmental burdens associated with a complete vehicle life cycle. This would be more effective from both environmental and resource management perspectives, than a simple life extension strategy. A transition to a new vehicle fleet with newer technology presents not only ecological issues but also social and economic issues. Fleet conversion often relies on individuals’ decisions to scrap their vehicles, and these decisions are generally based on economic considerations. For example, if bounties to recruit high-emitters are lower than the market prices of high-emitters, participation in scrappage programs will be minimal. In addition, scrapping older cars can damage the mobility of low-income people by
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raising the price of older cars. Thus, although the findings of this study are restricted to environmental aspects of fleet conversion policies, there are clearly important social and economic aspects to fleet conversions. Scrappage programs and I/M programs can be complementary to each other if they are implemented together. Emission properties of vehicles depend not only on vehicle age (mileage) but also on driving and maintenance customs, climates, manufacturers, etc. Thus, a scrappage program based on age (mileage) criteria would often entail uncertainties in its benefits. Accurate emission measurements would help improve the economic effectiveness of scrappage programs, because, ideally, the bounties for the scrapped cars are paid in exchange for emission reductions. The emission reductions quantified by exact measurements could also be traded in stationary source emissions market (EDF and GM 1998). In addition, strict I/M programs may increase the cost of operating old, high-emitting vehicles and, therefore, encourage owners to scrap those vehicles (ECMT 1999). Finally, based on the analysis in this study, scrapping those vehicles that fail I/M tests would be an efficient scrappage strategy. In particular, selectively scrapping specific high-emitter types, such as HC/CO high-emitters, would be most effective, due to the large contribution of those high- emitters to overall fleet emissions. The implications of this study apply to vehicle design as well as to fleet conversion policies. For instance, more robust designs of emission control systems, if combined with stringent inspection and maintenance programs, would reduce deterioration of emission performance. The enhanced durability of emission control systems would result in longer optimal vehicle lifetimes, as determined by the regulated auto emissions, than those determined by the simulation results of this study. This implies that better vehicle design and I/M policies would make scrappage policies less important, because the enhanced durability of emissions control systems would achieve significant emission reductions. In a similar way, enhancing fuel economies would
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change optimal lifetimes for CO2/energy in this study (18 years based on driving 12,000 miles annually) towards those of real-world cars (12-17 years). Thus, improving fuel economies would potentially eliminate a concern regarding scrappage programs: increasing life cycle CO2/energy caused by additional vehicle scrapping and manufacturing (ECMT 1999; Wee et al. 2000). If vehicle scrappage programs trigger additional vehicle sales as discussed by previous studies, such strategies would promote both business and environmental quality (Bohn 1992; ECMT 1999). This study investigates sustainable vehicle fleet conversion policies from environmental perspectives. The methods used in this study feature interdisciplinary modeling based on automobile engineering, life cycle environmental analysis, risk analysis, and operational engineering. Policy implications discussed in this study, therefore, are intended to help various audiences, including vehicle design engineers, policy makers, and consumers, understand the impacts of vehicle retirement/replacement decisions on the environment.
6.3 Future Research
The conceptual models used in Chapters 2, 3, and 4 of this study can be applied to new vehicle technologies or to other products. The transition from a vehicle fleet with conventional propulsion systems to a fleet with new propulsion systems presents similar resource management issues as those addressed in this study. For example, fuel cell vehicle technology is currently being widely studied as a future transportation option, due to its minimal emissions during operations. However, large-scale manufacturing of fuel cell vehicles, although perhaps different from conventional vehicles in impact categories, would still create significant environmental burdens. Moreover, the introduction of fuel cell vehicles is currently limited by economics and performance issues. These conditions will lead to the following questions for life cycle optimization: when is the optimal time
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to replace conventional vehicles with fuel cell vehicles?; what are the optimal design lifetimes (or durability) for fuel cell vehicles?; how efficient should a fuel cell vehicle be? The models used in this study can help policy makers, manufacturers, and consumers understand the importance of these questions and guide decision-makers to reduce life cycle environmental burdens during a possible transition to fuel cell or other transportation systems. Note that a comprehensive estimation of life cycle environmental burdens is a prerequisite to the models in this study. Thus, more LCA studies regarding future technologies and their feasibility would also be critical for the expansion of this study to new, alternative vehicle systems. In the real world, most decision-making problems are characterized by multiple objectives. As shown in Chapters 3 and 4 of this study, the conflicting objectives
(between regulated pollutants and CO2/energy) require a further tradeoff analysis for a replacement decision. Although not included in this study, economic considerations are often important objectives in vehicle retirement decisions. Therefore, a multi-objective analysis based on both environmental and economic considerations would provide a better decision guide for policy makers, consumers, and manufacturers. In the near future, I/M tests will rely upon more On-Board Diagnostic (OBD) tests than conventional I/M emission tests (Lindner 2002). The OBD system, which equipped in all vehicles with model year 1996 and newer, monitors major emission control systems, and illuminates a Malfunction Indicator Light (MIL) if it detects a failure. The deterioration behavior and repair durability under future I/M programs will be quite different from those under current I/M programs. It would seem, therefore, that further investigation is needed regarding the fleet emission characteristics and I/M benefits of OBD-equipped vehicles.
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APPENDICES
179 APPENDIX A
DYNAMIC LIFE CYCLE INVENTORIES
Table A1: Dynamic LCIs for the materials production phase of a generic vehicle, BM(i)
Year Energy (MJ) CO2 (kg) CO (g) NMHC (g) NOx (g) 1985 94131 5239 66281 6324 13791 1986 93474 5194 65168 6288 13692 1987 93182 5161 64857 6278 13681 1988 91934 5080 63563 6192 13490 1989 90369 4967 62550 6082 13318 1990 88858 4849 61971 6002 13150 1991 85990 4645 60084 5831 12832 1992 86862 4705 59756 5878 13022 1993 86701 4679 59529 5875 13019 1994 86835 4675 59140 5884 13058 1995 85593 4604 58765 5788 12958 1996 84768 4533 58877 5724 12896 1997 83264 4445 57352 5596 12758 1998 82378 4387 56688 5512 12711 1999 82716 4405 57158 5515 12850 2000 82160 4351 57199 5474 12830 2001 81057 4269 56382 5428 12650 2002 79756 4174 55465 5369 12445 2003 78764 4101 54733 5330 12281 2004 77842 4037 54024 5287 12130 2005 76753 3960 53321 5231 11973 2006 75886 3900 52714 5185 11848 2007 74994 3838 52132 5138 11719 2008 74226 3786 51597 5097 11611 2009 73560 3742 51110 5059 11514 2010 72506 3664 50153 5023 11339 2011 71015 3584 49256 4917 11115 2012 69439 3499 48253 4806 10881 2013 68064 3424 47416 4710 10673 2014 66603 3346 46526 4606 10454 2015 66418 3306 45742 4651 10392 2016 63939 3207 44908 4413 10057 2017 62608 3136 44134 4316 9860 2018 61377 3071 43377 4228 9674 2019 60111 3004 42622 4136 9487 2020 58993 2946 41980 4058 9311
180 Table A2: Dynamic LCIs for the manufacturing phase of a generic vehicle, BA(i)
Year Energy (MJ) CO2 (kg) CO (g) HC (g) NOx (g) 1985 36959 2423 5501 13102 7809 1986 36601 2399 5448 12975 7733 1987 36527 2394 5437 12948 7718 1988 36240 2375 5394 12847 7657 1989 36295 2379 5402 12866 7669 1990 36664 2403 5457 12997 7747 1991 36067 2364 5368 12785 7620 1992 37740 2474 5617 13379 7974 1993 38684 2536 5758 13713 8173 1994 39728 2604 5913 14083 8394 1995 39894 2615 5938 14142 8429 1996 40198 2635 5983 14250 8493 1997 39464 2587 5874 13990 8338 1998 39584 2595 5892 14032 8364 1999 39260 2573 5844 13917 8295 2000 38819 2544 5778 13761 8202 2001 38669 2535 5756 13708 8170 2002 37833 2480 5631 13411 7993 2003 37316 2446 5554 13228 7884 2004 36801 2412 5478 13046 7776 2005 35774 2345 5325 12681 7558 2006 34810 2282 5181 12340 7355 2007 34011 2229 5062 12057 7186 2008 33119 2171 4930 11740 6998 2009 32338 2120 4813 11463 6833 2010 31452 2062 4681 11149 6645 2011 30578 2004 4551 10840 6461 2012 29716 1948 4423 10534 6279 2013 28914 1895 4304 10250 6109 2014 28076 1840 4179 9953 5932 2015 27250 1786 4056 9660 5757 2016 26482 1736 3942 9388 5595 2017 25725 1686 3829 9119 5435 2018 24934 1634 3711 8839 5268 2019 24199 1586 3602 8578 5113 2020 23476 1539 3494 8322 4960
181 (i,j), based on driving 12,000 miles annually: Energy (GJ) (i,j), based on driving 12,000 miles U Vehicle Age e phase of a generic vehicle, B 1 2 3 4 5 6 7 8 9 10 1113 12 14 15 16 17 18 19 20 Table A3-1: Dynamic LCIs for the us Table A3-1: Dynamic MY 1985 89.1 89.1 89.11986 89.1 86.2 86.2 86.21987 89.1 86.2 85.6 85.6 85.61988 89.1 86.2 85.6 84.1 84.1 84.11989 89.1 86.2 85.6 84.1 85.6 85.6 85.61990 89.1 86.2 85.6 84.1 85.6 86.5 86.5 86.5 89.11991 86.2 85.6 84.1 85.6 86.5 85.9 85.9 85.9 89.1 89.1 86.21992 85.6 84.1 85.6 86.5 85.9 87.1 87.1 87.1 89.1 86.2 86.2 85.61993 84.1 85.6 86.5 85.9 87.1 89.5 85.3 85.3 85.3 86.6 85.6 86.0 84.11994 85.6 86.5 85.9 87.1 89.6 85.3 86.7 85.6 85.6 85.6 86.1 84.5 84.6 86.01995 86.9 86.3 87.6 90.0 85.7 87.1 86.0 86.5 85.0 85.0 85.4 85.0 86.1 86.5 87.01996 86.4 87.7 90.0 85.8 87.1 86.1 86.5 85.5 84.9 85.0 85.0 85.4 85.5 86.5 87.4 87.4 86.81997 88.1 90.2 86.2 87.3 86.5 86.7 85.9 85.1 85.9 86.7 84.7 85.1 85.2 85.6 87.6 86.8 87.0 88.0 90.2 1998 86.1 87.3 86.5 86.7 85.8 85.1 85.8 86.7 85.5 87.6 84.4 84.8 84.9 85.3 85.2 87.0 88.2 88.2 86.3 87.3 1999 86.7 86.7 86.0 85.1 86.0 86.7 85.7 87.6 85.4 87.0 85.7 85.8 86.2 86.1 86.3 88.2 86.3 86.3 86.7 86.9 2000 86.0 85.4 86.0 86.9 85.7 87.9 85.4 87.2 86.3 88.5 84.3 84.7 84.6 84.8 84.8 86.6 86.7 86.9 86.0 85.6 2001 86.0 87.1 85.7 88.0 85.4 87.4 86.3 88.7 84.8 86.8 83.5 83.7 83.7 87.1 86.3 86.5 86.3 87.0 2002 86.0 87.9 85.7 87.3 86.6 88.6 85.1 86.7 83.9 87.0 82.9 83.1 83.1 83.4 86.4 86.5 86.4 86.2 87.9 2003 85.9 87.2 86.8 88.5 85.3 86.6 84.1 86.9 83.5 86.3 79.3 79.3 79.6 79.7 86.3 86.1 86.0 85.8 87.2 2004 86.7 88.4 85.2 86.6 84.0 86.9 83.4 86.2 79.6 86.2 79.1 79.3 79.5 79.4 85.9 85.7 85.6 86.6 88.4 2005 85.1 86.5 84.0 86.8 83.4 86.2 79.6 86.2 79.3 85.9 78.8 79.0 79.2 79.1 79.1 85.6 86.6 86.5 85.0 86.4 2006 83.9 86.7 83.3 86.1 79.5 86.1 79.2 85.8 79.0 85.5 78.5 78.7 78.6 78.5 86.4 85.0 84.9 83.8 86.8 2007 83.3 86.2 79.5 86.2 79.2 85.9 78.9 85.6 78.4 86.5 77.7 77.6 77.6 77.5 77.4 85.0 83.7 83.8 83.1 86.1 2008 79.3 86.1 79.1 85.8 78.8 85.5 78.3 86.4 77.3 84.9 77.1 77.0 76.8 83.7 83.2 83.2 79.4 86.1 2009 79.2 85.8 78.9 85.5 78.4 86.4 77.4 84.9 76.9 83.7 76.3 76.2 76.1 76.2 83.1 79.4 79.3 79.1 85.7 2010 78.9 85.4 78.3 86.3 77.4 84.8 76.9 83.7 76.1 83.1 75.6 75.5 75.4 75.5 75.4 79.3 79.1 79.0 78.8 85.3 2011 78.3 86.2 77.3 84.7 76.8 83.5 76.1 83.0 75.4 79.2 75.3 75.2 75.3 75.2 75.2 78.9 78.8 78.7 78.3 86.1 77.3 84.6 76.8 83.5 76.1 82.9 75.4 79.1 75.1 78.9 78.6 78.2 78.1 77.2 84.6 76.7 83.4 76.0 82.8 75.3 79.0 75.0 78.8 78.5 78.0 77.1 77.0 76.6 83.3 75.9 82.7 75.2 79.0 75.0 78.7 78.5 78.0 77.0 76.5 76.5 75.8 82.7 75.1 79.0 74.9 78.7 78.5 78.0 77.0 76.5 75.8 75.8 75.1 79.0 74.8 78.7 78.5 78.0 77.0 76.5 75.8 75.1 75.1 74.8 78.7 78.5 78.0 77.0 76.5 75.8 75.1 74.8 74.8 78.5 78.0 77.0 76.5 75.8 75.1 74.8 78.0 77.0 76.5 75.8 75.1 74.8 77.0 76.5 75.8 75.1 74.8 76.5 75.8 75.1 74.8 75.8 75.1 74.8 75.1 74.8 74.8
182 (kg) (kg) 2 (i,j), based on driving 12,000 miles annually: CO (i,j), based on driving 12,000 miles U Vehicle Age Vehicle Age Table A3-1: Continued e phase of a generic vehicle, B 1 2 3 4 5 6 7 8 9 10 1113 12 14 151 2 3 4 5 16 6 17 7 18 19 8 20 9 10 1113 12 14 15 16 17 18 19 20
Table A3-2: Dynamic LCIs for the us Table A3-2: Dynamic MY 2012 75.0 75.0 74.9 74.92013 74.8 75.3 75.2 75.2 75.1 75.02014 74.7 75.0 75.2 75.1 75.0 75.02015 74.7 74.9 74.9 75.2 75.1 75.0 74.92016 74.6 74.8 74.8 74.8 75.1 75.0 75.0 74.9 74.82017 74.6 74.8 74.8 74.8 74.8 75.0 74.9 74.8 74.8 74.62018 74.8 74.8 74.8 74.8 74.8 75.0 74.9 74.8 74.8 74.6 74.6 74.82019 74.8 74.8 74.8 74.8 74.8 74.9 74.8 74.8 74.8 74.6 74.8 74.8 74.82020 74.8 74.8 74.8 74.8 74.8 74.6 74.6 74.6 74.6 74.8 74.8 74.8 74.8 74.8 74.8 74.8 74.8 74.6 74.6 74.8 74.8 74.8 74.8 74.8 74.8 74.8 74.8 74.6 74.6 74.8 74.8 74.8 74.8 74.8 74.8 74.8 74.8 74.6 74.6 74.8 74.8 74.8 74.8 74.8 74.8 74.8 74.8 74.6 74.6 74.8 74.8 74.8 74.8 74.8 74.8 74.8 74.8 74.6 74.6 74.8 74.8 74.8 74.8 74.8MY 74.8 74.8 74.8 74.6 74.8 74.8 74.8 74.8 74.8 74.8 74.8 74.6 74.6 74.8 74.81985 74.8 74.8 74.8 74.8 74.6 5926 5926 5926 74.8 74.81986 74.8 74.8 5926 74.8 74.6 5735 5735 5735 74.8 74.8 59261987 74.8 74.8 5735 74.6 5694 5694 5694 5926 74.8 74.8 57351988 74.8 74.6 5694 5926 5594 5594 5594 5735 74.8 74.8 5694 59261989 74.6 5594 5735 5694 5694 5694 5694 5926 74.8 74.6 5594 57351990 5694 5694 5926 5926 5755 5755 5755 5594 5735 74.6 5694 5694 59261991 5755 5594 5735 5735 5714 5714 5714 5694 5694 5954 5755 5594 57621992 5714 5694 5694 5721 5961 5797 5797 5797 5755 5594 5768 5714 5694 5727 59891993 5797 5755 5621 5627 5796 5674 5674 5674 5714 5721 5755 5985 5797 5783 5654 57921994 5674 5741 5727 5755 5751 5999 5694 5694 5694 5824 5789 5650 5805 5701 5748 5751 57641995 5999 5721 5831 5817 5813 5663 5805 5654 5654 5680 5707 5775 5764 5764 5727 5859 5826 56631996 5805 5687 5734 5771 5785 5764 5764 5654 5654 5680 5687 5755 5855 5826 5663 5714 5731 5785 5764 5782 5714 5751 5868 5868 5826 5681 5710 5744 5785 5782 5710 5764 5868 5844 5692 5723 5744 5744 5802 5794 5723 5764 5886 5856 5723 5761 5814 5785 5723 5764 5782 5899 5848 5723 5773 5806 5723 5794 5890 5845 5741 5753 5765 5803 5741 5785 5887 5745 5762 5798 5753 5745 5783 5882 5742 5757 5742 5778 5879 5737 5754 5737 5774 5734 5746 5734 5766 5725 5725 5772 5731 5731 5727 5727 5725
183
Vehicle Age Table A3-2: Continued 1 2 3 4 5 6 7 8 9 10 1113 12 14 15 16 17 18 19 20 MY 1997 5634 5660 5667 56941998 5690 5614 5640 5647 5674 5670 57031999 5683 5701 5707 5734 5731 5744 5703 56832000 5744 5703 5727 5755 5751 5764 5764 5683 5744 57212001 5764 5700 5557 5553 5566 5566 5761 5732 5782 57122002 5583 5773 5724 5722 5515 5528 5528 5545 5794 5704 5595 5765 57172003 5556 5785 5702 5697 5276 5276 5292 5303 5587 5762 5713 5548 5783 56932004 5295 5584 5757 5754 5705 5259 5275 5286 5278 5546 5778 5685 5293 5579 5746 57112005 5276 5541 5774 5766 5691 5242 5258 5269 5261 5259 5288 5576 5751 5707 5271 5538 5772 56872006 5254 5285 5568 5573 5747 5705 5224 5235 5227 5225 5220 5268 5530 5768 5685 5251 5278 5570 57452007 5702 5217 5261 5535 5532 5766 5682 5168 5161 5159 5154 5151 5244 5283 5568 5742 5210 5266 5530 57622008 5674 5144 5249 5279 5277 5564 5734 5128 5126 5122 5119 5111 5215 5262 5526 5755 5149 5245 5274 55572009 5730 5116 5212 5260 5257 5519 5750 5078 5074 5071 5063 5068 5145 5243 5267 5553 5113 5210 5250 55152010 5744 5065 5143 5240 5233 5263 5547 5026 5024 5016 5021 5018 5111 5207 5246 5509 5063 5140 5229 52582011 5542 5016 5108 5200 5196 5241 5504 5008 5001 5006 5002 5000 5060 5134 5224 5253 5013 5101 5190 52362012 5504 4998 5053 5130 5124 5219 5253 4986 4990 4987 4985 4982 5006 5097 5186 5236 4991 5050 5120 52192013 5253 4976 5003 5092 5088 5186 5236 5006 5002 5000 4998 4991 4987 5044 5120 5219 4972 4997 5088 51862014 5236 4987 4982 5040 5040 5120 5219 5002 5000 4998 4991 4987 4967 4993 5088 5186 4982 4978 5040 51202015 5219 4982 4962 4993 4993 5088 5186 5000 4998 4991 4987 4982 4978 4978 5040 5120 4978 4962 4993 50882016 5186 4978 4978 4978 4978 5040 5120 4998 4991 4987 4982 4978 4978 4962 4993 5088 4978 4978 4978 50402017 5120 4978 4978 4962 4962 4993 5088 4991 4987 4982 4978 4978 4978 4978 4978 5040 4978 4978 4962 49932018 5088 4978 4978 4978 4978 4978 5040 4987 4982 4978 4978 4978 4978 4962 4993 4978 4978 4978 49782019 5040 4978 4978 4978 4978 4962 4993 4982 4978 4978 4978 4978 4978 4978 4978 4978 4978 4978 49622020 4993 4978 4978 4978 4978 4978 4978 4962 4962 4962 4978 4978 4978 4962 4978 4978 4978 4978 4978 4962 4978 4978 4978 4978 4962 4978 4978 4978 4978 4962 4978 4978 4978 4962 4978 4978 4978 4978 4978 4962 4978 4978 4978 4978 4978 4978 4978 4962 4978 4978 4978 4978 4978 4978 4978 4962 4978 4978 4978 4978 4978 4978 4978 4962 4978 4978 4978 4978 4978 4962 4962 4978 4978 4978 4978 4962 4978 4978 4978 4978 4962 4978 4978 4978 4962 4978 4978 4962 4978 4978 4962 4978 4962 4962
184 miles annually: CO (kg) miles (i,j), based on driving 12,000 U Vehicle Age e phase of a generic vehicle, B 1 2 3 4 5 6 7 8 9 10 1113 12 14 15 16 17 18 19 20 Table A3-3: Dynamic LCIs for the us Table A3-3: Dynamic MY 1985 79.3 1986 147.9 132.0 97.8 115.3 162.9 77.3 1987 177.3 139.1 124.8 94.0 109.8 123.2 190.9 152.7 75.5 111.7 134.11988 203.8 165.6 130.9 118.1 90.4 104.6 73.7 87.1 99.7 144.4115.9 216.2 177.9 143.1 105.8 154.2 125.6 227.91989 189.5 154.7 72.1 83.9 95.1 163.6 134.7249.6 239.0 109.0 200.6 165.7 172.5 100.1 143.4 259.6 117.5 211.21990 176.2 70.5 80.9 90.8 180.9196.6 151.7 269.2 125.5230.7 221.2 186.1 188.9 203.8 159.6 278.3101.5 133.2 239.8 195.61991 67.8 76.9 85.5 93.7 167.1 210.7 286.9 108.8 140.5181.0 248.4213.1 204.6 217.3 174.2 295.2 115.9 147.4 187.4 256.6 221.31992 223.5 303.0 65.2 73.0 80.4 87.5 94.2 122.6 154.0 193.5 264.4166.2 229.0 229.5 310.4 100.6 128.9 160.2 199.3 271.8 171.8 236.41993 235.1 106.6 135.0 204.8 278.9 62.6 69.2 75.5 81.5 87.2 177.2 243.4 240.5 112.4 140.7 210.1 285.6 151.4 182.3 250.01994 92.6 117.9 146.2 215.1 156.3 187.2 256.3 60.1 65.6 70.8 75.7 80.4 123.1 219.8 161.0 191.8 262.3 1995 97.7 128.1 84.9137.2 165.5 196.1 57.6 62.0 66.1 70.1 73.9 102.6 132.8 141.5 200.3 169.71996 107.3 89.1 145.5 173.7 77.4 58.1 62.0 65.7 69.2 72.6 111.7 149.4 177.6 93.2 115.91997 123.7 80.8 153.0 181.2 75.8 58.6 62.0 65.3 68.4 71.4 119.9 127.3 156.5 97.0 84.1 130.71998 159.8 78.8 74.1 59.1 62.1 64.9 67.6 70.1 100.7 133.9 163.0 104.1 87.1110.6 137.0 81.71999 76.8 72.5 59.6 62.1 64.5 66.7 68.9 107.4 113.6 140.0 90.0 116.4 84.4 142.8 79.32000 74.8 70.9 58.9 60.9 62.8 64.7 66.4 119.1 145.4 92.8 87.0 121.7 81.7 77.02001 72.8 68.0 124.1 57.7 59.7 61.6 63.4 65.298.0 95.5 89.5 126.4 84.0 79.1 74.62002 100.3 69.6 66.8 128.6 56.5 58.5 60.4 62.2 64.0 102.694.1 91.9 86.2 81.0 76.4 71.1 104.72003 68.4 65.6 55.3 57.3 59.2 61.0 62.8 96.2 106.890.2 88.3 82.9 78.0 72.5 108.7 69.92004 67.2 64.4 54.0 56.0 57.9 59.7 61.5 98.2 110.6 92.186.4 84.7 79.6 73.8 100.2 71.3 112.3 68.72005 66.0 63.1 52.7 54.7 56.6 58.4 60.1 102.0 93.9 88.082.5 81.1 75.1 72.6 103.7 70.1 67.42006 64.6 61.8 95.6 105.4 51.5 53.5 55.4 57.1 58.8 89.5 83.977.5 76.3 73.9 106.9 71.4 68.8 66.12007 63.3 97.2 60.5 91.0 50.2 52.2 54.1 55.8 57.5 85.2 78.676.3 75.1 72.7 70.2 67.5 64.82008 98.7 62.0 92.4 59.1 86.4 50.1 52.0 53.9 55.6 57.3 79.6 77.4 100.175.1 73.9 71.5 68.8 66.2 63.5 101.5 2009 93.7 60.7 87.6 58.9 80.6 49.9 51.8 53.7 55.4 57.1 78.4 76.173.8 72.7 70.1 67.5 64.8 94.9 62.12010 88.7 60.4 81.5 58.7 79.4 49.8 51.7 53.5 55.2 56.9 77.2 74.972.5 71.3 68.8 66.2 96.1 63.5 89.7 61.82011 82.4 60.2 80.3 58.4 78.2 49.6 51.5 53.3 55.0 56.6 76.0 73.571.1 70.0 67.4 64.8 90.7 63.2 83.3 61.6 81.2 59.9 79.1 58.2 76.9 74.6 72.269.7 68.6 66.1 64.5 84.1 62.9 82.1 61.3 80.0 59.6 77.9 75.6 73.2 70.868.4 67.2 65.7 64.2 82.9 62.6 80.9 61.0 78.8 76.5 74.2 71.8 69.468.0 66.9 65.4 63.9 81.7 62.4 79.6 77.4 75.1 72.8 70.5 69.167.7 66.6 65.1 63.6 80.4 78.2 76.0 73.7 71.4 70.1 68.767.4 66.3 64.8 79.0 76.8 74.6 72.3 71.1 69.7 68.467.0 66.0 77.6 75.4 73.2 72.0 70.7 69.4 68.1 76.2 74.0 72.8 71.6 70.3 69.0 74.8 73.7 72.5 71.2 70.0 74.4 73.3 72.1 70.9 74.1 72.9 71.7 73.7 72.5 73.3
185 (i,j), based on driving 12,000 miles annually: NMHC (kg) (i,j), based on driving 12,000 miles U Vehicle Age Vehicle Age Table A3-3: Continued e phase of a generic vehicle, B 1 2 3 4 5 6 7 8 9 10 1113 12 14 15 16 17 18 19 20 1 2 3 4 5 6 7 8 9 10 1113 12 14 15 16 17 18 19 20
Table A3-4: Dynamic LCIs for the us Table A3-4: Dynamic MY 2012 49.5 51.3 53.1 54.8 56.42013 57.9 49.3 51.2 52.9 54.6 56.22014 59.4 57.7 49.2 51.0 52.7 54.4 56.0 60.82015 59.1 57.5 49.0 50.8 52.5 54.2 55.7 62.1 60.52016 58.9 57.2 48.8 50.6 52.3 54.0 55.5 63.3 61.8 60.22017 58.6 57.0 48.7 50.5 52.2 53.8 55.3 64.5 63.0 61.5 60.02018 58.4 56.7 48.5 50.3 52.0 53.6 55.1 65.6 66.7 64.2 62.7 61.2 59.72019 58.1 56.5 48.4 50.1 51.8 53.3 54.8 67.7 65.3 66.4 63.9 62.4 61.0 59.42020 57.9 56.3 48.2 50.0 51.6 53.1 54.6 68.7 67.4 65.0 66.1 63.6 62.2 60.7 59.2 57.6 56.0 69.6 68.4 67.1 64.7 65.7 63.3 61.9 60.4 58.9 57.4 70.5 69.3 68.0 66.7 64.4 65.4 63.0 61.6 60.1 58.6 71.3 70.1 68.9 67.7 66.4 64.1 65.1 62.7 61.3 59.8 72.1 71.0 69.8 68.6 67.3 66.0 63.7 64.8 62.4 61.0 72.9 MY 71.7 70.6 69.4 68.2 67.0 65.7 63.4 64.4 62.1 72.5 71.4 70.2 69.0 67.8 66.61985 65.4 63.1 64.1 18.0 19.6 21.2 22.7 24.1 72.1 71.0 69.8 68.7 67.5 66.31986 65.0 25.5 17.7 19.1 20.5 21.8 23.1 71.7 70.6 69.5 68.3 67.1 65.91987 26.7 24.3 17.4 18.7 19.9 21.1 22.2 71.3 70.2 69.1 67.9 66.81988 28.0 25.4 23.2 17.2 18.3 19.3 20.3 21.3 70.9 69.8 68.7 67.6 29.11989 26.5 24.2 22.2 15.5 16.4 17.3 18.2 19.0 70.5 69.5 68.3 30.2 27.51990 25.1 23.0 19.7 13.8 14.6 15.3 16.0 16.7 70.2 69.1 31.3 28.5 26.01991 23.9 20.5 17.3 12.1 12.8 13.4 14.1 14.7 69.8 32.3 33.2 29.4 26.9 24.61992 21.2 17.9 15.2 10.5 11.1 11.6 12.2 12.7 34.1 30.3 31.1 27.7 25.4 21.81993 18.5 15.7 13.2 10.3 10.8 11.3 11.7 35.012.2 32.0 28.5 29.2 26.1 22.5 19.11994 16.2 13.6 12.6 35.8 10.0 10.4 10.8 11.1 32.711.5 30.0 26.8 27.4 23.1 19.6 16.81995 14.1 13.0 36.6 11.9 33.5 30.7 28.1 23.7 24.3 20.1 9.6 17.2 14.51996 37.4 13.4 12.2 34.2 9.9 10.2 10.5 10.8 31.3 28.6 24.8 20.7 21.1 17.7 9.5 14.9 13.8 38.1 34.8 12.5 11.1 31.9 9.8 10.0 10.3 10.6 29.2 25.3 21.6 18.1 18.5 15.3 14.1 38.7 12.8 35.4 11.4 32.5 10.8 29.7 25.7 22.0 18.9 15.7 16.0 14.4 13.1 11.6 36.0 33.0 11.1 30.2 26.2 22.4 19.3 16.3 14.7 15.0 13.4 11.9 11.3 33.5 30.7 26.6 22.7 19.6 16.7 15.3 13.6 13.9 12.1 11.5 31.2 27.0 23.1 20.0 17.0 15.6 14.1 12.3 11.7 27.4 23.4 20.3 17.2 15.8 14.3 12.5 12.7 11.9 23.7 20.5 17.5 16.0 14.5 12.9 12.1 12.2 20.8 17.7 16.2 14.7 13.1 12.4 18.0 16.5 14.9 13.2 12.5 16.6 15.0 13.4 12.6 13.5 15.2 12.7 13.6 12.9 13.8 13.0 13.1
186
Vehicle Age Table A3-4: Continued 1 2 3 4 5 6 7 8 9 10 1113 12 14 15 16 17 18 19 20
MY 10.310.1 1997 9.4 9.6 9.9 10.510.11998 10.7 9.3 9.5 9.7 9.9 10.31999 10.9 9.2 9.4 9.6 9.7 9.9 10.42000 11.1 10.0 9.1 9.3 9.4 9.5 9.6 10.6 11.32001 10.1 9.0 9.2 9.3 9.4 9.5 10.7 9.7 11.42002 10.311.7 9.0 9.1 9.2 9.3 9.4 10.9 9.6 11.6 10.4 9.82003 11.8 11.0 8.9 9.0 9.1 9.2 9.3 9.611.2 10.5 9.72004 11.9 11.1 9.9 8.2 8.3 8.4 8.5 8.6 11.3 9.4 10.6 9.6 12.02005 10.8 10.0 9.8 7.5 7.6 7.7 7.8 11.4 10.7 8.6 12.2 9.52006 10.8 10.1 9.7 6.8 6.9 7.0 7.0 11.5 9.9 7.9 12.3 10.9 10.2 8.72007 9.6 11.6 5.7 5.8 5.910.3 9.8 12.4 7.1 11.0 10.2 9.9 7.92008 11.7 8.8 10.4 5.6 5.7 5.7 9.6 12.4 5.9 11.1 10.0 9.9 7.12009 11.8 10.410.2 8.0 5.4 5.5 5.6 8.8 11.1 5.8 10.1 9.7 6.02010 11.8 10.5 10.2 9.9 7.2 5.3 5.4 5.5 11.2 8.0 5.610.0 8.9 5.8 10.52011 10.3 10.0 9.8 6.0 5.2 5.3 5.3 11.3 10.1 7.2 5.5 10.6 8.1 10.3 5.72012 8.9 5.8 5.0 5.1 5.2 10.2 9.8 9.9 6.0 5.4 10.6 10.4 7.3 5.52013 8.1 5.7 10.2 4.9 5.0 5.0 9.0 9.0 5.9 5.2 10.4 10.7 9.9 6.1 5.42014 7.3 10.3 5.5 4.8 4.9 4.9 8.2 8.2 5.7 10.5 5.1 10.0 9.1 5.9 5.22015 10.3 6.1 5.4 4.7 4.8 7.3 7.4 10.5 5.6 10.0 4.9 8.2 5.7 5.12016 10.3 9.1 5.9 5.3 4.5 4.6 4.6 10.1 6.1 6.1 5.4 4.8 7.4 5.6 5.02017 10.4 8.3 5.8 5.1 4.4 4.5 4.5 10.1 9.2 6.0 6.0 5.3 4.7 6.2 5.4 4.82018 7.4 5.6 10.2 5.0 4.3 4.4 4.4 8.3 5.8 5.8 5.1 4.5 9.2 6.0 5.3 4.72019 6.2 5.5 10.2 4.9 4.2 4.2 7.4 5.6 5.7 5.0 4.4 8.3 5.8 5.2 4.62020 9.3 6.0 5.3 4.7 4.0 4.1 4.1 6.2 5.5 5.5 4.9 4.3 7.5 5.7 5.0 4.4 8.4 5.9 5.2 4.6 9.3 6.1 5.4 5.4 4.7 4.1 6.2 5.6 4.9 4.3 7.5 5.7 5.1 4.4 8.4 5.9 5.2 5.2 4.6 6.1 9.3 5.4 4.8 4.2 6.3 5.6 4.9 4.3 7.5 5.7 5.1 5.1 4.5 5.9 8.4 5.3 4.6 6.1 5.4 4.8 4.2 6.3 5.6 4.9 5.0 4.3 5.8 7.6 5.1 4.5 5.9 5.3 4.6 6.1 5.4 4.8 4.8 4.2 5.6 6.3 5.0 4.4 5.8 5.1 4.5 6.0 5.3 4.7 4.7 5.5 6.1 4.8 4.2 5.6 5.0 4.4 5.8 5.2 4.5 4.5 5.3 6.0 4.7 5.5 4.9 4.2 5.7 5.0 4.4 4.4 5.2 5.8 4.6 5.3 4.7 5.5 4.9 4.2 4.3 5.0 5.7 4.4 5.2 4.6 5.4 4.7 4.9 5.5 4.3 5.1 4.4 5.2 4.6 4.8 5.4 4.9 4.3 5.1 4.4 4.6 5.2 4.8 4.9 4.3 4.5 5.1 4.6 4.8 4.3 4.9 4.5 4.6 4.8 4.3 4.5 4.6 4.3 4.5 4.3
187 (i,j), based on driving 12,000 miles annually: NOx (kg) (i,j), based on driving 12,000 miles U Vehicle Age e phase of a generic vehicle, B 1 2 3 4 56789101112 1314151617181920 56789101112 4 3 2 1 Table A3-5: Dynamic LCIs for the us Table A3-5: Dynamic MY 1985 17.5 18.8 19.9 14.9 16.3 1986 21.016.9 18.1 19.3 14.3 15.7 22.01987 20.316.4 17.6 18.7 13.8 15.1 23.0 21.41988 19.715.8 17.0 18.1 13.3 14.6 24.0 22.3 20.71989 19.115.3 16.5 17.6 12.9 14.1 24.9 23.3 21.7 20.11990 18.614.8 16.0 17.0 12.4 13.7 25.7 24.1 22.6 21.1 19.61991 18.114.1 15.2 16.3 11.6 12.9 26.5 27.3 25.0 23.5 22.0 20.5 19.01992 17.413.4 14.6 15.7 10.9 12.2 28.0 25.8 26.5 24.3 22.8 21.4 19.9 18.31993 16.712.6 13.8 14.9 10.1 11.4 28.8 27.3 25.1 25.9 23.6 22.2 20.8 19.3 17.71994 16.0 29.5 28.0 26.6 24.5 25.211.9 13.1 14.2 9.3 10.6 23.1 21.7 20.2 18.7 17.11995 30.2 28.7 27.4 26.0 15.4 23.9 24.7 8.5 22.5 21.1 19.6 18.011.2 12.4 13.6 9.9 30.81996 29.4 28.0 26.6 16.4 25.4 23.3 24.1 8.6 22.0 20.6 19.0 14.7 31.410.9 12.1 13.2 9.8 30.01997 28.7 27.3 17.5 26.0 24.8 22.7 23.5 8.6 21.4 19.9 15.8 31.9 14.2 30.510.8 11.8 12.8 9.7 29.21998 27.9 18.4 26.7 25.4 24.2 22.2 22.9 8.6 20.7 16.8 15.2 31.1 13.7 29.810.6 11.5 12.3 9.6 28.51999 19.3 27.3 26.0 24.8 23.6 21.5 22.3 8.7 17.8 16.1 14.6 30.4 13.2 29.110.4 11.2 12.0 9.6 20.1 27.92000 26.6 25.5 24.3 23.0 18.7 8.3 17.0 15.410.3 13.9 29.6 12.7 20.9 21.7 28.4 9.1 27.22001 26.19.1 9.7 7.8 8.5 25.0 23.7 9.8 10.5 11.1 19.5 17.8 16.2 10.9 14.7 13.3 28.9 22.5 27.7 26.62002 25.68.5 9.1 9.6 7.3 7.9 24.3 11.7 20.4 21.2 18.6 16.9 11.5 15.4 14.0 23.2 28.2 27.2 26.12003 10.2 24.97.8 8.3 8.8 6.7 7.2 12.4 21.9 19.4 20.0 17.7 12.0 16.1 14.7 23.8 27.7 26.7 10.6 25.52004 12.96.9 7.3 7.7 22.6 6.0 6.5 20.7 18.3 18.9 12.4 16.7 9.2 15.2 24.4 27.2 11.1 26.1 13.42005 23.26.0 6.3 6.5 5.4 5.7 21.3 19.5 12.9 17.3 17.8 15.8 25.0 8.0 11.5 9.7 26.6 13.9 23.82006 21.95.1 5.3 5.4 4.8 5.0 20.1 13.3 18.3 16.3 16.7 25.5 6.814.1 11.9 10.1 8.3 14.4 24.4 22.52007 20.6 13.7 4.2 4.1 18.8 26.1 17.2 5.5 14.4 12.3 10.4 14.8 15.2 25.0 7.0 23.0 21.12008 19.313.0 8.64.1 4.2 4.1 17.7 14.8 4.3 12.6 10.8 25.5 15.7 23.5 5.6 21.6 19.8 13.32009 18.1 7.24.1 4.2 4.0 4.1 15.1 11.2 8.9 4.2 16.0 24.0 22.1 4.3 20.2 13.611.8 18.52010 5.84.0 4.1 4.0 15.4 11.5 7.4 4.2 16.4 22.5 9.2 20.6 4.3 13.9 12.1 18.82011 4.34.0 4.1 3.9 4.0 15.7 16.7 5.9 4.1 21.0 14.2 12.3 7.6 19.2 4.2 16.0 9.5 4.3 17.0 4.4 4.1 14.4 12.6 19.5 6.0 4.1 16.2 10.0 7.8 4.2 17.3 9.7 4.3 14.7 12.9 4.4 4.1 10.2 17.6 6.1 4.2 14.9 8.0 8.1 4.2 13.1 4.3 10.4 4.4 4.1 13.3 6.2 6.3 4.2 10.6 8.3 4.3 4.3 13.5 4.4 4.4 4.1 10.8 6.4 4.2 8.4 4.3 4.3 4.4 11.0 4.4 4.1 6.4 4.2 8.5 4.3 4.3 11.1 4.4 4.4 4.2 11.3 6.5 4.2 4.2 8.7 4.3 4.4 4.5 4.2 4.2 6.6 4.3 8.8 4.3 4.4 4.5 4.2 6.7 4.3 8.9 4.4 4.4 4.5 4.3 6.7 4.3 9.1 4.4 4.5 4.5 4.3 6.8 4.3 4.4 4.5 4.5 4.3 4.3 4.4 4.5 4.3 4.4 4.4 4.3 4.4 4.3
188 000 miles annually: Energy (GJ) 000 miles (i,j), based on driving 12, R
Vehicle Age Vehicle Age Table A3-5: Continued intenance of a generic vehicle, B 1 2 3 4 56789101112 1314151617181920 56789101112 4 3 2 1 1 2 3 4 56789101112 1314151617181920 56789101112 4 3 2 1 Table A4-1: Dynamic LCIs for the ma Table A4-1: Dynamic MY 2012 4.0 3.9 4.0 2013 4.0 3.9 4.02014 3.9 4.0 3.9 4.0 4.12015 3.9 3.9 4.0 4.02016 4.13.9 3.8 3.9 3.9 4.02017 4.03.8 3.9 3.8 4.1 3.9 4.02018 4.03.8 3.9 3.8 4.1 3.9 4.1 3.92019 4.03.8 3.8 4.0 3.9 4.1 3.92020 4.1 4.03.8 3.7 4.0 3.9 4.1 3.9 4.1 3.9 4.2 4.2 4.0 3.8 4.0 3.9 4.1 3.9 4.1 4.1 4.0 4.2 4.0 3.8 4.1 3.9 4.1 4.1 3.9 4.2 4.0 4.2 4.0 3.9 4.1 4.1 3.9MY 4.1 3.9 4.2 4.0 4.2 4.0 4.1 3.9 4.1 3.91.51 1.531.53 1.52 1985 4.2 4.0 4.2 4.0 4.0 4.2 4.1 3.9 1.541.53 1.541.53 1.52 1.51 1986 4.1 3.9 4.2 4.0 4.0 4.2 4.0 1.56 1.561.54 1.561.52 1.51 1.53 4.3 1987 4.1 3.9 4.1 3.9 4.0 4.2 4.0 1.59 1.59 1.591.56 1.591.51 1.53 1.54 4.2 1988 4.1 4.3 4.1 3.9 3.9 1.62 4.1 4.0 1.62 1.62 1.621.59 1.621.53 1.54 1.56 4.21989 4.0 4.2 4.1 1.66 4.3 1.66 4.1 3.9 1.66 1.66 1.661.62 1.661.54 1.56 1.59 4.2 1990 4.0 4.2 4.0 1.64 1.64 4.3 1.64 4.1 1.64 1.64 1.641.66 1.641.56 1.59 1.62 4.1 1991 3.9 4.2 4.0 1.64 1.61 1.64 1.64 4.2 1.64 4.0 1.64 1.64 1.641.64 1.59 1.62 1.66 4.1 1992 1.60 4.1 4.0 1.61 1.60 1.61 1.61 4.2 1.61 4.0 1.61 1.61 1.611.64 1.611.62 1.66 1.64 4.1 1993 1.59 1.59 4.1 1.60 1.59 1.60 1.60 4.2 1.60 4.0 1.60 1.60 1.601.61 1.601.66 1.64 4.0 1994 1.56 1.56 1.56 4.1 1.59 1.56 1.59 1.59 4.1 1.59 1.59 1.59 1.59 1.561.60 1.591.64 1.61 4.0 1995 1.56 1.56 1.56 4.0 1.56 1.56 1.56 4.1 1.56 1.56 1.56 1.53 1.56 1.53 1.53 1.53 1.53 4.0 1.56 1.53 1.56 1.56 4.0 1.56 1.56 1.51 1.56 1.51 1.51 1.51 1.51 1.51 1.53 1.51 1.53 1.53 4.0 1.53 1.53 1.50 1.50 1.50 1.50 1.50 1.50 1.50 1.51 1.50 1.51 1.51 1.51 1.47 1.47 1.47 1.47 1.47 1.47 1.47 1.50 1.47 1.50 1.50 1.44 1.44 1.44 1.44 1.44 1.44 1.44 1.47 1.44 1.47 1.42 1.42 1.42 1.42 1.42 1.42 1.42 1.44 1.42 1.39 1.39 1.39 1.39 1.39 1.39 1.39 1.37 1.37 1.37 1.37 1.37 1.37 1.35 1.35 1.35 1.35 1.35 1.33 1.33 1.33 1.33 1.31 1.31 1.31 1.29 1.29 1.27
189 Vehicle Age Table A4-1: Continued 1 2 3 4 5 6 7 8 9 10 1113 12 14 15 16 17 18 19 20
MY 1996 1.64 1.61 1.60 1.59 1.561997 1.56 1.61 1.60 1.59 1.56 1998 1.53 1.53 1.60 1.59 1.56 1.53 1.511999 1.51 1.51 1.59 1.56 1.56 1.53 1.51 1.50 1.502000 1.50 1.50 1.56 1.53 1.51 1.50 1.47 1.47 1.472001 1.47 1.47 1.56 1.53 1.51 1.50 1.47 1.44 1.44 1.44 1.442002 1.44 1.44 1.53 1.51 1.50 1.47 1.441.39 1.42 1.42 1.42 1.42 1.422003 1.42 1.42 1.51 1.50 1.47 1.44 1.42 1.371.37 1.39 1.39 1.39 1.39 1.392004 1.39 1.39 1.50 1.47 1.44 1.42 1.39 1.35 1.351.35 1.37 1.37 1.37 1.37 1.372005 1.37 1.37 1.33 1.47 1.44 1.42 1.39 1.37 1.33 1.331.33 1.35 1.35 1.35 1.35 1.352006 1.35 1.31 1.35 1.31 1.44 1.42 1.39 1.37 1.35 1.31 1.311.31 1.33 1.33 1.33 1.33 1.33 1.292007 1.33 1.29 1.33 1.29 1.42 1.39 1.37 1.35 1.33 1.29 1.291.29 1.31 1.31 1.31 1.31 1.27 1.31 1.272008 1.31 1.27 1.31 1.27 1.39 1.37 1.35 1.33 1.31 1.27 1.271.27 1.29 1.29 1.29 1.24 1.29 1.24 1.29 1.242009 1.29 1.24 1.29 1.24 1.37 1.35 1.33 1.31 1.29 1.24 1.241.24 1.27 1.27 1.27 1.22 1.27 1.22 1.27 1.222010 1.27 1.22 1.27 1.22 1.35 1.33 1.31 1.29 1.27 1.22 1.221.22 1.24 1.24 1.24 1.24 1.21 1.21 1.24 1.212011 1.24 1.21 1.24 1.21 1.33 1.31 1.29 1.27 1.24 1.21 1.211.21 1.22 1.22 1.22 1.22 1.18 1.18 1.22 1.182012 1.22 1.18 1.22 1.18 1.31 1.29 1.27 1.24 1.22 1.18 1.181.18 1.21 1.21 1.21 1.21 1.16 1.16 1.21 1.162013 1.21 1.16 1.21 1.16 1.29 1.27 1.24 1.22 1.21 1.16 1.161.16 1.18 1.18 1.18 1.18 1.15 1.15 1.18 1.152014 1.18 1.15 1.18 1.15 1.27 1.24 1.22 1.21 1.18 1.15 1.151.15 1.16 1.16 1.16 1.15 1.16 1.15 1.16 1.152015 1.16 1.15 1.16 1.15 1.24 1.22 1.21 1.18 1.16 1.15 1.151.15 1.15 1.15 1.15 1.15 1.15 1.15 1.15 1.152016 1.15 1.15 1.15 1.15 1.22 1.21 1.18 1.16 1.15 1.15 1.151.15 1.15 1.15 1.15 1.15 1.15 1.15 1.15 1.152017 1.15 1.15 1.15 1.15 1.21 1.18 1.16 1.15 1.15 1.151.15 1.15 1.15 1.15 1.15 1.15 1.15 1.15 1.152018 1.15 1.15 1.15 1.15 1.18 1.16 1.15 1.15 1.151.15 1.15 1.15 1.15 1.15 1.15 1.15 1.15 1.152019 1.15 1.15 1.15 1.15 1.16 1.15 1.15 1.15 1.151.15 1.15 1.15 1.15 1.15 1.15 1.15 1.15 1.152020 1.15 1.15 1.15 1.15 1.15 1.15 1.15 1.151.15 1.15 1.15 1.15 1.15 1.15 1.15 1.15 1.15 1.15 1.15 1.15 1.15 1.15 1.151.15 1.15 1.15 1.15 1.15 1.15 1.15 1.15 1.15 1.15 1.15 1.15 1.15 1.151.15 1.15 1.15 1.15 1.15 1.15 1.15 1.15 1.15 1.15 1.15 1.15 1.151.15 1.15 1.15 1.15 1.15 1.15 1.15 1.15 1.15 1.15 1.15 1.151.15 1.15 1.15 1.15 1.15 1.15 1.15 1.15 1.15 1.15 1.151.15 1.15 1.15 1.15 1.15 1.15 1.15 1.15 1.15 1.151.15 1.15 1.15 1.15 1.15 1.15 1.15 1.15 1.15 1.15 1.15 1.15 1.15 1.15 1.15 1.15 1.15 1.15 1.15 1.15 1.15 1.15 1.15 1.15 1.15 1.15 1.15 1.15 1.15 1.15
190 (kg) 2 (i,j), based on driving 12,000 miles annually: CO (i,j), based on driving 12,000 miles R Vehicle Age intenance of a generic vehicle, B 1 2 3 4 56789101112 1314151617181920 56789101112 4 3 2 1 Table A4-2: Dynamic LCIs for the ma Table A4-2: Dynamic MY 1985 57.2 57.0 56.7 56.5 57.01986 57.6 57.0 56.7 56.5 57.61987 58.2 58.2 56.7 56.5 57.0 57.6 58.2 59.41988 59.4 59.4 56.5 57.0 57.6 58.2 59.4 60.6 60.61989 60.6 60.6 57.0 57.6 58.2 59.4 60.6 61.8 61.8 61.81990 61.8 61.8 57.6 58.2 59.4 60.6 61.8 61.4 61.4 61.4 61.41991 61.4 61.4 58.2 59.4 60.6 61.8 61.4 61.3 59.9 61.3 61.3 61.3 61.31992 61.3 61.3 59.4 60.6 61.8 61.4 61.3 59.9 59.9 59.9 59.9 59.9 59.91993 59.9 59.9 60.6 61.8 61.4 61.3 59.9 59.2 59.2 59.9 59.2 59.9 59.9 59.9 59.91994 59.9 59.9 58.3 61.8 61.4 61.3 59.9 59.9 58.3 58.3 59.2 58.3 59.2 59.2 59.2 59.21995 59.2 58.2 59.2 58.2 61.4 61.3 59.9 59.2 58.2 58.2 58.3 58.2 58.3 58.3 58.3 58.3 57.11996 58.3 57.1 58.3 57.1 61.3 59.9 59.2 58.3 57.1 57.1 58.2 57.1 58.2 58.2 58.2 56.4 58.2 56.41997 58.2 56.4 58.2 56.4 59.9 59.2 58.3 58.2 56.4 56.4 57.1 56.4 57.1 57.1 57.1 55.8 55.8 57.1 55.81998 57.1 55.8 57.1 55.8 59.9 59.2 58.3 58.2 57.1 55.8 55.8 56.4 55.8 56.4 56.4 56.4 54.7 54.7 56.4 54.71999 56.4 54.7 56.4 54.7 59.2 58.3 58.2 57.1 56.4 54.7 54.7 55.8 54.7 55.8 55.8 55.8 53.7 53.7 55.8 53.72000 55.8 53.7 55.8 53.7 58.3 58.2 57.1 56.4 55.8 53.7 53.7 54.7 53.7 54.7 54.7 54.7 52.9 52.9 54.7 52.92001 54.7 52.9 54.7 52.9 58.2 57.1 56.4 55.8 54.7 52.9 52.9 53.7 52.9 53.7 53.7 53.7 51.9 51.9 53.7 51.92002 53.7 51.9 53.7 51.9 57.1 56.4 55.8 54.7 53.7 51.9 51.9 52.9 51.9 52.9 52.9 52.9 51.1 51.1 52.9 51.12003 52.9 51.1 52.9 51.1 56.4 55.8 54.7 53.7 52.9 51.1 51.1 51.9 51.1 51.9 51.9 51.9 50.3 50.3 51.9 50.32004 51.9 50.3 51.9 50.3 55.8 54.7 53.7 52.9 51.9 50.3 50.3 51.1 50.3 51.1 51.1 51.1 49.5 49.5 51.1 49.52005 51.1 49.5 51.1 49.5 54.7 53.7 52.9 51.9 51.1 49.5 49.5 50.3 49.5 50.3 50.3 50.3 48.7 48.7 50.3 48.72006 50.3 48.7 50.3 48.7 53.7 52.9 51.9 51.1 50.3 48.7 48.7 49.5 48.7 49.5 49.5 49.5 48.0 48.0 49.5 48.02007 49.5 48.0 49.5 48.0 52.9 51.9 51.1 50.3 49.5 48.0 48.0 48.7 48.0 48.7 48.7 48.7 47.2 47.2 48.7 47.22008 48.7 47.2 48.7 47.2 51.9 51.1 50.3 49.5 48.7 47.2 47.2 48.0 47.2 48.0 48.0 48.0 46.4 46.4 48.0 46.42009 48.0 46.4 48.0 46.4 51.1 50.3 49.5 48.7 48.0 46.4 46.4 47.2 46.4 47.2 47.2 47.2 45.7 45.7 47.2 45.72010 47.2 45.7 47.2 45.7 50.3 49.5 48.7 48.0 47.2 45.7 45.7 46.4 45.7 46.4 46.4 46.4 45.0 45.0 46.4 45.02011 46.4 45.0 46.4 45.0 49.5 48.7 48.0 47.2 46.4 45.0 45.0 45.7 45.0 45.7 45.7 45.7 44.2 44.2 45.7 44.2 45.7 44.2 45.7 44.2 44.2 44.2 45.0 44.2 45.0 45.0 45.0 43.5 43.5 45.0 43.5 45.0 43.5 43.5 43.5 43.5 44.2 43.5 44.2 44.2 44.2 42.7 42.7 44.2 42.7 42.7 42.7 42.7 42.7 43.5 42.7 43.5 43.5 43.5 42.7 42.7 42.7 42.7 42.7 42.7 42.7 42.7 42.7 42.7 42.7 42.7 42.7 42.7 42.7 42.7 42.7 42.7 42.7 42.7 42.7 42.7 42.7 42.7 42.7 42.7 42.7 42.7 42.7 42.7 42.7 42.7 42.7 42.7 42.7 42.7 42.7 42.7 42.7 42.7 42.7 42.7 42.7 42.7 42.7 42.7 42.7 42.7 42.7 42.7 42.7 42.7 42.7 42.7 42.7
191 (i,j), based on driving 12,000 miles annually: CO (kg) annually: CO (i,j), based on driving 12,000 miles R
Vehicle Age Vehicle Age Table A4-2: Continued intenance of a generic vehicle, B 1 2 3 4 56789101112 1314151617181920 56789101112 4 3 2 1 1 2 3 4 56789101112 1314151617181920 56789101112 4 3 2 1 Table A4-3: Dynamic LCIs for the ma Table A4-3: Dynamic MY 2012 48.7 48.0 47.2 46.4 45.72013 45.0 48.0 47.2 46.4 45.7 45.02014 44.2 44.2 47.2 46.4 45.7 45.0 44.2 43.52015 43.5 43.5 46.4 45.7 45.0 44.2 43.5 42.7 42.72016 42.7 42.7 45.7 45.0 44.2 43.5 42.7 42.7 42.7 42.72017 42.7 42.7 45.0 44.2 43.5 42.7 42.7 42.7 42.7 42.7 42.72018 42.7 42.7 44.2 43.5 42.7 42.7 42.7 42.7 42.7 42.7 42.72019 42.7 42.7 43.5 42.7 42.7 42.7 42.7 42.7 42.7 42.7 42.72020 42.7 42.7 42.7 42.7 42.7 42.7 42.7 42.7 42.7 42.7 42.7 42.7 42.7 42.7 42.7 42.7 42.7 42.7 42.7 42.7 42.7 42.7 42.7 42.7 42.7 42.7 42.7 42.7 42.7 42.7 42.7 42.7 42.7 42.7 42.7 42.7 42.7 42.7 42.7 42.7 42.7 42.7 42.7 42.7 42.7 42.7 42.7 MY 42.7 42.7 42.7 42.7 42.7 42.7 42.7 42.7 42.7 42.7 42.7 42.7 42.7 42.71985 42.7 42.7 42.7 42.7 42.7 3.64 3.63 3.61 3.60 3.63 42.7 42.7 42.71986 42.7 42.7 42.7 42.7 3.67 3.63 3.61 3.60 3.63 3.67 42.7 42.7 42.71987 42.7 42.7 42.7 3.71 3.71 3.61 3.60 3.63 3.67 3.71 42.7 42.7 42.71988 42.7 42.7 3.78 3.78 3.78 3.60 3.63 3.67 3.71 3.78 42.7 42.7 42.71989 42.7 3.86 3.86 3.86 3.86 3.63 3.67 3.71 3.78 3.86 42.7 42.7 42.7 3.941990 3.94 3.94 3.94 3.94 3.67 3.71 3.78 3.86 3.94 42.7 42.7 3.91 3.911991 3.91 3.91 3.91 3.91 3.71 3.78 3.86 3.94 3.91 3.90 3.82 42.7 3.90 3.901992 3.90 3.90 3.90 3.90 3.78 3.86 3.94 3.91 3.90 3.81 3.82 3.81 3.82 3.821993 3.82 3.82 3.82 3.82 3.77 3.86 3.94 3.91 3.90 3.82 3.77 3.81 3.77 3.81 3.811994 3.81 3.81 3.81 3.71 3.81 3.71 3.94 3.91 3.90 3.82 3.81 3.71 3.77 3.71 3.77 3.771995 3.77 3.77 3.71 3.77 3.71 3.77 3.71 3.91 3.90 3.82 3.81 3.77 3.71 3.71 3.71 3.711996 3.71 3.64 3.71 3.64 3.71 3.64 3.71 3.64 3.90 3.82 3.81 3.77 3.71 3.64 3.71 3.64 3.71 3.71 3.60 3.71 3.60 3.71 3.60 3.71 3.60 3.71 3.60 3.60 3.64 3.60 3.64 3.56 3.64 3.56 3.64 3.56 3.64 3.56 3.64 3.56 3.56 3.56 3.60 3.56 3.60 3.48 3.60 3.48 3.60 3.48 3.60 3.48 3.48 3.48 3.48 3.56 3.48 3.56 3.42 3.56 3.42 3.56 3.42 3.42 3.42 3.42 3.42 3.48 3.42 3.48 3.37 3.48 3.37 3.37 3.37 3.37 3.37 3.37 3.42 3.37 3.42 3.31 3.31 3.31 3.31 3.31 3.31 3.31 3.37 3.31 3.26 3.26 3.26 3.26 3.26 3.26 3.26 3.20 3.20 3.20 3.20 3.20 3.20 3.15 3.15 3.15 3.15 3.15 3.10 3.10 3.10 3.10 3.06 3.06 3.06 3.01 3.01 2.96
192 Vehicle Age Table A4-3: Continued 1 2 3 4 56789101112 1314151617181920 56789101112 4 3 2 1
MY 1997 3.82 3.81 3.77 3.71 1998 3.64 3.81 3.77 3.71 3.71 3.641999 3.60 3.60 3.77 3.71 3.64 3.602000 3.56 3.56 3.56 3.71 3.71 3.64 3.60 3.562001 3.48 3.48 3.48 3.48 3.71 3.64 3.60 3.56 3.48 3.422002 3.42 3.42 3.42 3.42 3.64 3.60 3.56 3.48 3.42 3.37 3.372003 3.37 3.37 3.37 3.37 3.60 3.56 3.48 3.42 3.37 3.31 3.26 3.31 3.312004 3.31 3.31 3.31 3.31 3.56 3.48 3.42 3.37 3.31 3.20 3.26 3.20 3.26 3.262005 3.26 3.26 3.26 3.26 3.15 3.48 3.42 3.37 3.31 3.26 3.15 3.20 3.15 3.20 3.202006 3.20 3.20 3.20 3.10 3.20 3.10 3.42 3.37 3.31 3.26 3.20 3.10 3.15 3.10 3.15 3.152007 3.15 3.15 3.06 3.15 3.06 3.15 3.06 3.37 3.31 3.26 3.20 3.15 3.06 3.10 3.06 3.10 3.102008 3.10 3.01 3.10 3.01 3.10 3.01 3.10 3.01 3.31 3.26 3.20 3.15 3.10 3.01 3.06 3.01 3.06 3.06 2.962009 3.06 2.96 3.06 2.96 3.06 2.96 3.06 2.96 3.26 3.20 3.15 3.10 3.06 2.96 3.01 2.96 3.01 2.91 3.01 2.912010 3.01 2.91 3.01 2.91 3.01 2.91 3.01 2.91 3.20 3.15 3.10 3.06 3.01 2.91 2.96 2.91 2.96 2.86 2.96 2.862011 2.96 2.86 2.96 2.86 2.96 2.86 2.96 2.86 3.15 3.10 3.06 3.01 2.96 2.86 2.91 2.86 2.91 2.81 2.91 2.812012 2.91 2.81 2.91 2.81 2.91 2.81 2.91 2.81 3.10 3.06 3.01 2.96 2.91 2.81 2.86 2.81 2.86 2.77 2.86 2.772013 2.86 2.77 2.86 2.77 2.86 2.77 2.86 2.77 3.06 3.01 2.96 2.91 2.86 2.77 2.81 2.77 2.81 2.72 2.81 2.722014 2.81 2.72 2.81 2.72 2.81 2.72 2.81 2.72 3.01 2.96 2.91 2.86 2.81 2.72 2.77 2.72 2.77 2.72 2.77 2.722015 2.77 2.72 2.77 2.72 2.77 2.72 2.77 2.72 2.96 2.91 2.86 2.81 2.77 2.72 2.72 2.72 2.72 2.72 2.722016 2.72 2.72 2.72 2.72 2.72 2.72 2.72 2.72 2.91 2.86 2.81 2.77 2.72 2.72 2.72 2.72 2.72 2.72 2.722017 2.72 2.72 2.72 2.72 2.72 2.72 2.72 2.72 2.86 2.81 2.77 2.72 2.72 2.72 2.72 2.72 2.72 2.722018 2.72 2.72 2.72 2.72 2.72 2.72 2.72 2.72 2.81 2.77 2.72 2.72 2.72 2.72 2.72 2.72 2.72 2.722019 2.72 2.72 2.72 2.72 2.72 2.72 2.72 2.72 2.77 2.72 2.72 2.72 2.72 2.72 2.72 2.72 2.722020 2.72 2.72 2.72 2.72 2.72 2.72 2.72 2.72 2.72 2.72 2.72 2.72 2.72 2.72 2.72 2.72 2.72 2.72 2.72 2.72 2.72 2.72 2.72 2.72 2.72 2.72 2.72 2.72 2.72 2.72 2.72 2.72 2.72 2.72 2.72 2.72 2.72 2.72 2.72 2.72 2.72 2.72 2.72 2.72 2.72 2.72 2.72 2.72 2.72 2.72 2.72 2.72 2.72 2.72 2.72 2.72 2.72 2.72 2.72 2.72 2.72 2.72 2.72 2.72 2.72 2.72 2.72 2.72 2.72 2.72 2.72 2.72 2.72 2.72 2.72 2.72 2.72 2.72 2.72 2.72 2.72 2.72 2.72 2.72 2.72 2.72 2.72 2.72 2.72 2.72 2.72 2.72 2.72 2.72 2.72 2.72 2.72 2.72 2.72 2.72 2.72 2.72 2.72 2.72 2.72 2.72 2.72 2.72 2.72 2.72
193 000 miles annually: NMHC (kg) annually: NMHC 000 miles (i,j), based on driving 12, R Vehicle Age intenance of a generic vehicle, B 1 2 3 4 56789101112 1314151617181920 56789101112 4 3 2 1 Table A4-4: Dynamic LCIs for the ma Table A4-4: Dynamic MY 1985 0.200 0.199 0.198 0.200 0.199 1986 0.202 0.204 0.202 0.198 0.200 0.199 0.199 0.2081987 0.204 0.212 0.208 0.204 0.200 0.202 0.199 0.198 0.216 0.2121988 0.208 0.215 0.216 0.212 0.208 0.202 0.204 0.198 0.200 0.210 0.215 0.215 0.2161989 0.212 0.210 0.215 0.215 0.216 0.212 0.204 0.208 0.200 0.202 0.207 0.210 0.210 0.215 0.2151990 0.216 0.204 0.207 0.210 0.215 0.215 0.216 0.208 0.212 0.202 0.204 0.204 0.204 0.207 0.210 0.210 0.2151991 0.215 0.200 0.204 0.204 0.210 0.210 0.215 0.198 0.215 0.212 0.216 0.204 0.208 0.200 0.204 0.204 0.207 0.210 0.2101992 0.215 0.198 0.195 0.200 0.204 0.207 0.210 0.210 0.195 0.215 0.216 0.215 0.208 0.212 0.198 0.200 0.204 0.204 0.207 0.2101993 0.210 0.195 0.191 0.198 0.200 0.204 0.207 0.210 0.191 0.210 0.215 0.215 0.212 0.216 0.195 0.198 0.200 0.204 0.204 0.2071994 0.210 0.191 0.188 0.195 0.198 0.204 0.204 0.207 0.188 0.210 0.215 0.210 0.216 0.215 0.191 0.195 0.198 0.200 0.204 0.2041995 0.207 0.188 0.185 0.191 0.195 0.200 0.204 0.204 0.185 0.207 0.210 0.210 0.215 0.215 0.188 0.191 0.195 0.198 0.200 0.2041996 0.204 0.185 0.182 0.188 0.191 0.198 0.200 0.204 0.182 0.204 0.210 0.207 0.215 0.210 0.185 0.188 0.191 0.195 0.198 0.2001997 0.204 0.182 0.179 0.185 0.188 0.195 0.198 0.200 0.179 0.204 0.207 0.204 0.210 0.210 0.182 0.185 0.188 0.191 0.195 0.1981998 0.200 0.179 0.176 0.182 0.185 0.191 0.195 0.198 0.176 0.200 0.204 0.204 0.210 0.207 0.179 0.182 0.185 0.188 0.191 0.1951999 0.198 0.176 0.173 0.179 0.182 0.188 0.191 0.195 0.173 0.198 0.204 0.200 0.207 0.204 0.176 0.179 0.182 0.185 0.188 0.1912000 0.195 0.173 0.171 0.176 0.179 0.185 0.188 0.191 0.171 0.195 0.200 0.198 0.204 0.204 0.173 0.176 0.179 0.182 0.185 0.1882001 0.191 0.171 0.168 0.173 0.176 0.182 0.185 0.188 0.168 0.191 0.198 0.195 0.204 0.200 0.171 0.173 0.176 0.179 0.182 0.1852002 0.188 0.168 0.165 0.171 0.173 0.179 0.182 0.185 0.165 0.188 0.195 0.191 0.200 0.198 0.168 0.171 0.173 0.176 0.179 0.1822003 0.185 0.165 0.162 0.168 0.171 0.176 0.179 0.182 0.162 0.185 0.191 0.188 0.198 0.195 0.165 0.168 0.171 0.173 0.176 0.1792004 0.182 0.162 0.160 0.165 0.168 0.173 0.176 0.179 0.160 0.182 0.188 0.185 0.195 0.191 0.162 0.165 0.168 0.171 0.173 0.1762005 0.179 0.160 0.157 0.162 0.165 0.171 0.173 0.176 0.157 0.179 0.185 0.182 0.191 0.188 0.160 0.162 0.165 0.168 0.171 0.1732006 0.176 0.157 0.155 0.160 0.162 0.168 0.171 0.173 0.155 0.176 0.182 0.179 0.188 0.185 0.157 0.160 0.162 0.165 0.168 0.1712007 0.173 0.155 0.152 0.157 0.160 0.165 0.168 0.171 0.152 0.173 0.179 0.176 0.185 0.182 0.155 0.157 0.160 0.162 0.165 0.1682008 0.171 0.152 0.150 0.155 0.157 0.162 0.165 0.168 0.150 0.171 0.176 0.173 0.182 0.179 0.152 0.155 0.157 0.160 0.162 0.1652009 0.168 0.150 0.150 0.152 0.155 0.160 0.162 0.165 0.150 0.168 0.173 0.171 0.179 0.176 0.150 0.152 0.155 0.157 0.160 0.1622010 0.165 0.150 0.150 0.150 0.152 0.157 0.160 0.162 0.150 0.165 0.171 0.168 0.176 0.173 0.150 0.150 0.152 0.155 0.157 0.1602011 0.162 0.150 0.150 0.150 0.150 0.155 0.157 0.160 0.150 0.162 0.168 0.165 0.173 0.171 0.150 0.150 0.150 0.152 0.155 0.157 0.160 0.150 0.150 0.150 0.150 0.152 0.155 0.157 0.150 0.150 0.150 0.150 0.150 0.152 0.155 0.150 0.150 0.150 0.150 0.150 0.152 0.150 0.150 0.150 0.150 0.150 0.150 0.150 0.150 0.150 0.150 0.150 0.150 0.150 0.150 0.150 0.150 0.150 0.150 0.150 0.150 0.150 0.150 0.150 0.150 0.150 0.150 0.150 0.150 0.150 0.150 0.150 0.150
194 000 miles annually: NOx (kg/year) annually: NOx 000 miles (i,j), based on driving 12, R
Vehicle Age Vehicle Age Table A4-4: Continued intenance of a generic vehicle, B 1 2 3 4 56789101112 1314151617181920 56789101112 4 3 2 1213141516171819 1 4567891011 3 2 1 0 Table A4-5: Dynamic LCIs for the ma Table A4-5: Dynamic MY 2012 0.160 0.165 0.162 0.171 0.168 2013 0.157 0.155 0.157 0.162 0.160 0.168 0.165 0.1522014 0.155 0.150 0.152 0.155 0.160 0.157 0.165 0.162 0.150 0.1502015 0.152 0.150 0.150 0.150 0.152 0.157 0.155 0.162 0.160 0.150 0.150 0.150 0.1502016 0.150 0.150 0.150 0.150 0.150 0.150 0.155 0.152 0.160 0.157 0.150 0.150 0.150 0.150 0.1502017 0.150 0.150 0.150 0.150 0.150 0.150 0.150 0.152 0.150 0.157 0.155 0.150 0.150 0.150 0.150 0.150 0.1502018 0.150 0.150 0.150 0.150 0.150 0.150 0.150 0.150 0.150 0.150 0.150 0.155 0.152 0.150 0.150 0.150 0.150 0.150 0.1502019 0.150 0.150 0.150 0.150 0.150 0.150 0.150 0.150 0.150 0.150 0.150 0.150 0.152 0.150 0.150 0.150 0.150 0.150 0.150 0.1502020 0.150 0.150 0.150 0.150 0.150 0.150 0.150 0.150 0.150 0.150 0.150 0.150 0.150 0.150 0.150 0.150 0.150 0.150 0.150 0.150 0.150 0.150 0.150 0.150 0.150 0.150 0.150 0.150 0.150 0.150 0.150 0.150 0.150 0.150 0.150 0.150 0.150 0.150 0.150 0.150 0.150 0.150 0.150 0.150 0.150 0.150 0.150 0.150 0.150 0.150 0.150 0.150 0.150 0.150 0.150 0.150 0.150 0.150 0.150 0.150 0.150 0.150MY 0.150 0.150 0.150 0.150 0.150 0.150 0.1501985 0.150 0.150 0.150 0.255 0.253 0.252 0.255 0.254 1986 0.150 0.257 0.260 0.257 0.252 0.255 0.254 0.253 0.2651987 0.260 0.271 0.265 0.260 0.255 0.257 0.253 0.252 0.276 0.2711988 0.265 0.274 0.276 0.271 0.265 0.257 0.260 0.252 0.255 0.268 0.274 0.274 0.2761989 0.271 0.267 0.274 0.274 0.276 0.271 0.260 0.265 0.255 0.257 0.264 0.267 0.268 0.274 0.2741990 0.276 0.260 0.264 0.268 0.274 0.274 0.276 0.265 0.271 0.257 0.260 0.260 0.260 0.264 0.267 0.268 0.2741991 0.274 0.255 0.260 0.260 0.267 0.268 0.274 0.252 0.274 0.271 0.276 0.260 0.265 0.255 0.260 0.260 0.264 0.267 0.2681992 0.274 0.252 0.249 0.255 0.260 0.264 0.267 0.268 0.249 0.274 0.276 0.274 0.265 0.271 0.252 0.255 0.260 0.260 0.264 0.2671993 0.268 0.249 0.244 0.252 0.255 0.260 0.264 0.267 0.244 0.268 0.274 0.274 0.271 0.276 0.249 0.252 0.255 0.260 0.260 0.2641994 0.267 0.244 0.240 0.249 0.252 0.260 0.260 0.264 0.240 0.267 0.274 0.268 0.276 0.274 0.244 0.249 0.252 0.255 0.260 0.2601995 0.264 0.240 0.236 0.244 0.249 0.255 0.260 0.260 0.236 0.264 0.268 0.267 0.274 0.274 0.240 0.244 0.249 0.252 0.255 0.2601996 0.260 0.236 0.232 0.240 0.244 0.252 0.255 0.260 0.232 0.260 0.267 0.264 0.274 0.268 0.236 0.240 0.244 0.249 0.252 0.255 0.260 0.232 0.228 0.236 0.240 0.249 0.252 0.255 0.228 0.232 0.236 0.240 0.244 0.249 0.252 0.228 0.225 0.232 0.236 0.244 0.249 0.225 0.228 0.232 0.236 0.240 0.244 0.225 0.221 0.228 0.232 0.240 0.221 0.225 0.228 0.232 0.236 0.221 0.217 0.225 0.228 0.217 0.221 0.225 0.217 0.214 0.221 0.214 0.217 0.214 0.211 0.211 0.207
195
Vehicle Age Table A4-5: Continued 0 1 2 3 4567891011 1213141516171819 4567891011 3 2 1 0 MY 1997 0.260 0.264 0.260 0.268 0.267 1998 0.255 0.252 0.255 0.260 0.260 0.267 0.264 0.2491999 0.252 0.244 0.249 0.252 0.260 0.255 0.264 0.260 0.240 0.2442000 0.249 0.236 0.240 0.244 0.249 0.255 0.252 0.260 0.260 0.228 0.232 0.236 0.2402001 0.244 0.225 0.232 0.236 0.240 0.244 0.252 0.249 0.260 0.255 0.221 0.225 0.228 0.232 0.2362002 0.240 0.217 0.221 0.228 0.232 0.236 0.240 0.249 0.244 0.255 0.252 0.214 0.217 0.221 0.225 0.228 0.2322003 0.236 0.211 0.214 0.217 0.225 0.228 0.232 0.207 0.236 0.244 0.240 0.252 0.249 0.211 0.214 0.217 0.221 0.225 0.2282004 0.232 0.207 0.204 0.211 0.214 0.221 0.225 0.228 0.204 0.232 0.240 0.236 0.249 0.244 0.207 0.211 0.214 0.217 0.221 0.2252005 0.228 0.204 0.201 0.207 0.211 0.217 0.221 0.225 0.201 0.228 0.236 0.232 0.244 0.240 0.204 0.207 0.211 0.214 0.217 0.2212006 0.225 0.201 0.197 0.204 0.207 0.214 0.217 0.221 0.197 0.225 0.232 0.228 0.240 0.236 0.201 0.204 0.207 0.211 0.214 0.2172007 0.221 0.197 0.194 0.201 0.204 0.211 0.214 0.217 0.194 0.221 0.228 0.225 0.236 0.232 0.197 0.201 0.204 0.207 0.211 0.2142008 0.217 0.194 0.191 0.197 0.201 0.207 0.211 0.214 0.191 0.217 0.225 0.221 0.232 0.228 0.194 0.197 0.201 0.204 0.207 0.2112009 0.214 0.191 0.191 0.194 0.197 0.204 0.207 0.211 0.191 0.214 0.221 0.217 0.228 0.225 0.191 0.194 0.197 0.201 0.204 0.2072010 0.211 0.191 0.191 0.191 0.194 0.201 0.204 0.207 0.191 0.211 0.217 0.214 0.225 0.221 0.191 0.191 0.194 0.197 0.201 0.2042011 0.207 0.191 0.191 0.191 0.191 0.197 0.201 0.204 0.191 0.207 0.214 0.211 0.221 0.217 0.191 0.191 0.191 0.194 0.197 0.2012012 0.204 0.191 0.191 0.191 0.191 0.194 0.197 0.201 0.191 0.204 0.211 0.207 0.217 0.214 0.191 0.191 0.191 0.191 0.194 0.1972013 0.201 0.191 0.191 0.191 0.191 0.191 0.194 0.197 0.191 0.201 0.207 0.204 0.214 0.211 0.191 0.191 0.191 0.191 0.191 0.1942014 0.197 0.191 0.191 0.191 0.191 0.191 0.191 0.194 0.191 0.197 0.204 0.201 0.211 0.207 0.191 0.191 0.191 0.191 0.191 0.1912015 0.194 0.191 0.191 0.191 0.191 0.191 0.191 0.191 0.191 0.194 0.201 0.197 0.207 0.204 0.191 0.191 0.191 0.191 0.191 0.1912016 0.191 0.191 0.191 0.191 0.191 0.191 0.191 0.191 0.191 0.191 0.197 0.194 0.204 0.201 0.191 0.191 0.191 0.191 0.191 0.1912017 0.191 0.191 0.191 0.191 0.191 0.191 0.191 0.191 0.191 0.191 0.194 0.191 0.201 0.197 0.191 0.191 0.191 0.191 0.191 0.1912018 0.191 0.191 0.191 0.191 0.191 0.191 0.191 0.191 0.191 0.191 0.191 0.191 0.197 0.194 0.191 0.191 0.191 0.191 0.191 0.1912019 0.191 0.191 0.191 0.191 0.191 0.191 0.191 0.191 0.191 0.191 0.191 0.191 0.194 0.191 0.191 0.191 0.191 0.191 0.191 0.1912020 0.191 0.191 0.191 0.191 0.191 0.191 0.191 0.191 0.191 0.191 0.191 0.191 0.191 0.191 0.191 0.191 0.191 0.191 0.191 0.191 0.191 0.191 0.191 0.191 0.191 0.191 0.191 0.191 0.191 0.191 0.191 0.191 0.191 0.191 0.191 0.191 0.191 0.191 0.191 0.191 0.191 0.191 0.191 0.191 0.191 0.191 0.191 0.191 0.191 0.191 0.191 0.191 0.191 0.191 0.191 0.191 0.191 0.191 0.191 0.191 0.191 0.191 0.191 0.191 0.191 0.191 0.191 0.191 0.191 0.191 0.191 0.191 0.191
196 (i,j): Energy (MJ) (MJ) (i,j): Energy E Vehicle Age -life phase of a generic vehicle, B of a generic vehicle, -life phase Table A5-1: Dynamic LCIs for the end-of Table A5-1: Dynamic 1 2 3 4 5 6 7 8 9 10 1113 12 14 15 16 17 18 19 20 MY 1985 2150 2150 21501986 2150 2139 2139 2139 21501987 2139 2144 2144 2144 2150 21391988 2144 2150 2136 2136 2136 2139 2144 21501989 2136 2139 2118 2118 2118 2144 2150 2136 21391990 2118 2144 2150 2150 2118 2118 2118 2136 2139 2118 2144 21501991 2118 2136 2139 2139 2063 2063 2063 2118 2144 2206 2118 2136 21941992 2063 2118 2144 2199 2219 2115 2115 2115 2118 2136 2207 2063 2118 2212 22761993 2115 2118 2191 2205 2263 2125 2125 2125 2063 2173 2269 2268 2115 2173 2261 22561994 2125 2117 2186 2242 2261 2295 2139 2139 2139 2170 2186 2253 2282 2179 2130 2234 22881995 2295 2194 2183 2242 2234 2280 2282 2164 2164 2220 2193 2184 2261 2288 2208 2238 2261 22801996 2282 2233 2248 2176 2202 2261 2288 2183 2183 2239 2253 2264 2231 2261 2280 2290 2241 2202 22611997 2324 2310 2256 2257 2257 2261 2316 2191 2247 2261 2319 2282 2267 2202 2296 2302 2283 2257 22961998 2341 2311 2309 2267 2267 2237 2321 2200 2257 2271 2328 2320 2330 2283 2293 2321 2338 2309 2303 22611999 2304 2348 2330 2283 2319 2317 2304 2265 2279 2337 2329 2357 2338 2309 2328 2244 2348 2330 2344 23012000 2299 2357 2338 2346 2371 2311 2239 2288 2346 2338 2366 2366 2348 2366 2327 2295 2357 2375 2354 23052001 2229 2366 2385 2392 2374 2321 2285 2340 2332 2360 2360 2394 2400 2348 2295 2403 2410 2369 23112002 2278 2397 2420 2383 2377 2338 2289 2326 2354 2354 2391 2428 2393 2358 2304 2422 2402 2367 23312003 2272 2416 2411 2387 2377 2351 2287 2348 2348 2385 2410 2405 2396 2360 2314 2399 2405 2370 23342004 2299 2393 2399 2386 2379 2343 2325 2342 2379 2404 2387 2393 2395 2353 2346 2387 2389 2362 23542005 2317 2381 2383 2388 2370 2364 2338 2323 2360 2385 2368 2362 2377 2382 2373 2346 2371 2376 2382 23562006 2333 2352 2370 2364 2376 2365 2342 2340 2365 2348 2343 2332 2364 2358 2374 2351 2345 2353 2368 23602007 2335 2326 2347 2370 2362 2369 2345 2346 2329 2323 2313 2307 2328 2364 2363 2354 2309 2358 2357 23622008 2328 2290 2339 2356 2351 2356 2337 2310 2304 2294 2287 2271 2320 2350 2350 2346 2301 2331 2344 23402009 2328 2282 2312 2345 2339 2334 2337 2285 2275 2268 2252 2263 2293 2326 2328 2331 2274 2308 2322 23252010 2324 2255 2289 2320 2304 2319 2318 2248 2241 2225 2236 2228 2270 2301 2313 2312 2251 2282 2294 23062011 2307 2224 2263 2285 2276 2300 2302 2214 2198 2209 2201 2197 2244 2266 2282 2296 2217 2247 2263 2290 2302 2190 2229 2257 2245 2272 2296 2202 2238 2253 2290 2175 2220 2235 2272 2296 2193 2226 2216 2253 2290 2166 2208 2235 2272 2181 2216 2253 2290 2155 2198 2198 2235 2272 2171 2216 2253 2145 2198 2235 2272 2171 2171 2216 2253 2145 2198 2235 2171 2216 2253 2145 2145 2198 2235 2171 2216 2145 2198 2235 2171 2216 2145 2198 2171 2216 2145 2198 2171 2145 2198 2171 2145 2171 2145 2145
197 (g) 2 (i,j): CO E Vehicle Age -life phase of a generic vehicle, B of a generic vehicle, -life phase Vehicle Age Table A5-1: Continued Table A5-2: Dynamic LCIs for the end-of Table A5-2: Dynamic 1 2 3 4 5 6 7 8 9 10 1113 12 14 15 16 17 18 19 20 1 2 3 4 5 6 7 8 9 10 1113 12 14 15 16 17 18 19 20
MY 2012 2171 2181 2174 2170 21632013 2148 2154 2147 2143 2136 2122 21402014 2113 2120 2116 2110 2095 2087 2128 21022015 2075 2119 2089 2083 2068 2060 2049 2092 2066 21192016 2040 2092 2056 2042 2033 2022 2013 2066 2119 2040 20922017 2013 2066 2119 2119 2015 2007 1996 1987 1987 2040 2092 2013 2066 21192018 1987 2040 2092 2092 1980 1969 1961 1961 2013 2066 2119 1987 2040 20922019 1961 2013 2066 2066 2119 1943 1934 1934 1934 1987 2040 2092 1961 2013 2066 21192020 1934 1987 2040 2040 2092 1908 1908 1908 1961 2013 2066 2119 1934 1987 2040 2092 1908 1961 2013 2013 2066 2119 1934 1987 2040 2092 1908 1961 2013 2066 2119 1934 1987 1987 2040 2092 1908 1961 2013 2066 1934 1987 2040 2092 1908 1961 1961 2013 2066 1934 1987 2040 1908 1961 2013 2066 1934 1934 1987 2040 1908 1961 2013 1934 1987 2040 1908 1908 1961 2013MY 1934 1987 1908 1961 2013 1934 1987 1908 1961 1934 1987 1985 1908 1961 142 142 142 1934 1908 1961 1986 1934 142 142 142 142 1908 1934 1987 1908 142 142 142 142 142 1908 1988 141 141 141 142 142 1421989 140 140 140 141 142 142 1421990 140 140 140 140 141 142 142 1421991 137 137 137 140 140 141 142 142 1421992 140 140 140 137 140 140 141 142 1421993 142 142 141 141 141 140 137 140 140 141 1421421994 142 142 142 142 142 141 140 137 140 140 1411995 146 142 145 143 143 143 147 146 145 144 144 140 144 1441996 146 145 146 145 145 145 148 149 146 147 148 146 145 145 141 145148 145 150 150 150 153 151 152 150 149 148 145148 148 148 149 150 149 152 151 150 149 148 148146 144 150 150 151 151 154 151 152 153 151 150149 149 146 150 150 151 151 151 154 153 152 151150 150 149 146 150 150 151 151 154 151 153154 151 152 152 148 152 152 153 154 157157 155 155 154 153 150 154 154 155 157 158 156 154 153 152 149 153 153 157 155 154 153 152 148 152 156 155 153 152 151 148 156 154 153 152 151 155 153 151 150 155 154 152 155 153 154
198
Vehicle Age Table A5-2: Continued 1 2 3 4 5 6 7 8 9 10 1113 12 14 15 16 17 18 19 20
MY 1997 145 145 149 150 1541998 146 149 150 154 1531999 150 151 155 154 156 155 1552000 151 155 155 157 156 155 1552001 155 154 156 157 156 155 1552002 154 156 156 158 159 159 159 158 1572003 155 155 155 158 160 160 160 161 160 160 1592004 155 155 157 159 158 158 159 159 160 159 1582005 157 158 154 156 158 157 156 158 158 158 159 159 1591572006 158 157 155 157 155 154 156 157 157 158 158 1592007 158 158 157 155 154 154 153 156 154 155 157 157 157 1582008 157 158 156 153 153 152 151 150 156 155 152 153 154 155 156 1562009 157 157 158 151 151 150 149 157 157 151 156 152 154 155 156 1572010 156 157 157 149 148 147 148 157 157 149 156 151 152 155 153 154 1562011 156 156 156 147 146 146 145 156 157 147 156 149 150 156 155 152 153 1541552012 155 155 144 144 144 143 156 156 145 156 147 149 156 155 150 151 155 1521532013 154 154 143 142 142 141 140 154 155 142 155 144 146 155 155 148 149 154 1502014 151 151 152 140 140 140 139 138 153 154 140 154 142 143 154 155 145 147 154 1482015 149 149 150 138 138 137 136 151 152 137 153 139 141 153 153 143 144 154 1462016 147 147 148 136 135 135 134 133 149 150 135 152 137 139 152 152 140 142 153 1442017 145 145 147 133 133 132 148 149 133 150 135 137 152 152 139 140 152 1422018 144 144 145 131 130 130 147 148 132 149 133 135 150 152 137 139 152 1401422019 142 144 129 128 128 145 147 130 148 132 133 149 150 135 137 152 1392020 140 140 142 126 126 126 144 145 128 147 130 132 148 149 133 135 150 137139 139 140 142 144 126 145 128 130 147 148 132 133 149 135137 137 139 140 142 144 126 128 145 147 130 132 148 133135 135 137 139 140 142 126 144 145 128 130 147 132133 133 135 137 139 140 142 144 126 128 145 130132 132 133 135 137 139 140 142 126 144 128130 130 132 133 135 137 139 140 142 126128 128 130 132 133 135 137 139 140 126 126 128 130 132 133 135 137 139 126 128 130 132 133 135 137 126 128 130 132 133 135 126 128 130 132 133 126 128 130 132 126 128 130 126 128 126
199 (i,j): CO (g) E Vehicle Age -life phase of a generic vehicle, B of a generic vehicle, -life phase Table A5-3: Dynamic LCIs for the end-of Table A5-3: Dynamic 1 2 3 4 5 6 7 8 9 10 1113 12 14 15 16 17 18 19 20 MY 1985 679 679 6791986 675 675 675 6791987 677 677 677 675 6791988 674 674 674 677 675 6791989 669 669 669 674 677 675 6791990 669 669 669 669 674 677 6751991 679 651 651 651 669 669 674 6771992 675 679 668 668 668 651 669 669 6741993 677 675 679 679 671 671 671 668 651 669 6691994 674 677 675 675 679 675 675 675 671 668 651 6691995 669 674 677 694 692 696 683 683 701 692 688 685 6681996 686 686 692 696 698 697 700 689 689 707 711 705 697 692 6891997 672 690 690 707 714 716 714 692 709 714 718732 729 723 714 7101998 706 689 708 705 705 711 714 694 712 717 735 712732 729 716 727 720 7121999 707 704 687 695 714 713 720 715 719 738 735 722744 741 720 738 724 735 7292000 721 716 712 712 695 714 713 722 740 738 747 747 720 744 722 741 720 738 724 7352001 729 721 716 716 712 695 714 739 736 745 745 713 747 720 744 722 741 720 7382002 735 729 721 732 727 724 706 734 743 743 725755 757 725 758 731 756 733 7532003 750 747 740 748 740 735 731 741 741 753 714761 763 733 765 732 766 739 7642004 761 758 755 749 743 734 729 739 751 759 726753 755 708 757 727 759 727 7612005 758 755 752 750 748 741 733 733 745 753 747 728745 752 724 753 707 755 726 7572006 759 756 753 750 747 744 738 739 747 741 739 729736 742 724 748 721 750 704 7522007 754 756 753 751 748 745 742 740 735 733 730 728 736 734 727 740 722 746 719 7482008 750 752 754 748 745 743 739 729 727 724 722 737717 723 730 729 722 735 717 7412009 742 744 746 750 752 749 746 721 718 716 711 743714 720 740 726 734 732 725 7382010 744 746 748 745 747 749 746 709 707 702 706 744703 712 741 718 738 724 731 7302011 736 742 744 742 744 746 748 699 694 697 695 745693 702 742 710 739 716 736 722 728 734 740 738 740 742 744 746 691 743 700 740 708 737 714 720 726 732 727 733 735 737 739 740 687 738 695 735 703 709 715 721 718 724 730 732 734 736 737 684 735 692 701 706 712 708 714 720 726 728 730 732 733 680 688 697 703 699 705 711 717 723 725 726 728 677 685 694 694 699 705 711 717 723 725 726 677 685 685 694 699 705 711 717 723 725 677 677 685 694 699 705 711 717 723 677 685 694 699 705 711 717 677 685 694 699 705 711 677 685 694 699 705 677 685 694 699 677 685 694 677 685 677
200 (i,j): NMHC (g) (i,j): NMHC E Vehicle Age Vehicle Age -life phase of a generic vehicle, B of a generic vehicle, -life phase Table A5-3: Continued Table A5-4: Dynamic LCIs for the end-of Table A5-4: Dynamic 1 2 3 4 5 6 7 8 9 10 1113 12 14 151 2 3 4 5 16 6 17 7 18 19 8 20 9 10 1113 12 14 15 16 17 18 19 20
MY 2012 685 689 686 685 6832013 680 678 676 674 670 6782014 669 668 666 661 659 667 6752015 659 657 653 650 647 655 663 6722016 649 644 642 638 635 644 652 660 6692017 636 633 630 627 627 635 644 652 6602018 669 625 622 619 619 627 635 644 6522019 660 669 613 610 610 610 619 627 635 6442020 652 660 669 669 602 602 602 610 619 627 635 644 652 660 660 669 602 610 619 627 635 644 652 652 660 669 602 610 619 627 635 644 644 652 660 669 602 610 619 627 635 635 644 652 660 669 602 610 619 627 627 635 644 652 660MY 669 602 610 619 619 627 635 644 652 660 669 6021985 610 610 619 627 635 644 652 170 170 170 660 669 1986 602 602 610 619 627 635 644 169 169 169 652 170 660 1987 602 610 619 627 635 169 169 169 644 169 652 1701988 602 610 619 627 169 169 169 635 169 644 169 1701989 602 610 619 167 167 167 627 169 635 169 169 1701990 602 610 167 167 167 619 167 627 169 169 1691991 170 602 163 163 163 610 167 619 167 169 1691992 169 170 167 167 167 602 163 610 167 167 1691993 169 169 170 170 168 168 168 167 602 163 167 1671994 169 169 169 169 170 169 169 169 168 167 163 1671995 167 169 169 174 173 174 171 171 175 173 172 171 1671996 172 172 173 174 175 174 175 172 172 177 178 176 174 173 172 168 173 173 177 179 179 179 180 183 181 179 178 177 173 177 177 177 178 179 178 179 182 180 178 177 176 172 174 179 179 180 181 180 181 184 182 180 179 178 178 174 179 179 180 181 180 181 184 182 180 179 179 178 174 179 179 180 181 180 184 182 180 183 182 181 177 181 181 183 184 187 185 187 185 184 183 179 183 183 185 189 188 186 184 183 182 177 182 182 187 186 183 182 181 177 182 186 185 183 181 181 176 186 184 182 181 180 184 183 181 180 185 184 182 185 183 184
201
Vehicle Age Table A5-4: Continued 1 2 3 4 5 6 7 8 9 10 1113 12 14 15 16 17 18 19 20 MY 1997 173 178 179 1831998 174 178 179 184 183 1831999 179 180 185 184 186 186 1852000 181 185 185 187 187 186 186 1852001 185 184 186 186 187 186 186 1852002 184 186 186 189 189 190 189 1882003 188 186 186 188 190 191 191 192 1912004 190 190 185 188 190 189 189 190 190 1912005 190 189 188 188 184 186 188 187 187 188 189 189 1902006 190 189 189 188 187 185 187 186 185 184 186 187 188 1882007 189 189 189 188 187 186 185 184 184 183 182 184 185 187 1872008 188 188 189 187 187 186 185 183 182 181 179 181 182 184 1852009 186 186 187 188 188 188 187 181 180 179 178 186179 180 182 183 1852010 186 187 187 187 187 188 187 178 177 176 177 186176 178 185 180 181 1832011 184 186 186 186 186 187 187 175 174 175 174 174 187 176 186 178 185 179 1812012 182 184 185 185 185 186 186 172 172 171 187171 173 186 175 185 177 184 1792013 180 182 183 182 184 184 184 170 169 185168 170 185 172 185 174 184 1762014 178 179 181 180 181 183 183 168 167 167 166 184165 167 184 169 185 171 184 1732015 175 177 178 177 179 180 182 165 163 182162 164 183 166 183 168 184 1702016 172 174 176 175 177 178 180 162 161 161 160 181159 161 181 163 182 165 182 1672017 169 172 174 174 175 177 178 159 158 157 180157 159 181 161 181 163 182 1652018 167 169 172 172 174 175 177 156 155 155 178 157 180 159 181 161 181 1632019 165 167 169 169 172 174 175 154 153 153 177153 155 178 157 180 159 181 1612020 163 165 167 167 169 172 174 151 151 175151 153 177 155 178 157 180 159 161 163 165 165 167 169 172 174 151 175 153 177 155 178 157 159 161 163 163 165 167 169 172 174 151 175 153 177 155 157 159 161 161 163 165 167 169 172 174 151 175 153 155 157 159 159 161 163 165 167 169 172 174 151 153 155 157 157 159 161 163 165 167 169 172 151 153 155 155 157 159 161 163 165 167 169 151 153 153 155 157 159 161 163 165 167 151 151 153 155 157 159 161 163 165 151 153 155 157 159 161 163 151 153 155 157 159 161 151 153 155 157 159 151 153 155 157 151 153 155 151 153 151
202 (i,j): NOx (g) E Vehicle Age -life phase of a generic vehicle, B of a generic vehicle, -life phase Table A5-5: Dynamic LCIs for the end-of Table A5-5: Dynamic 1 2 3 4 5 6 7 8 9 10 1113 12 14 15 16 17 18 19 20 MY 1985 801 801 8011986 797 797 797 8011987 798 798 798 797 8011988 796 796 796 798 797 8011989 789 789 789 796 798 797 8011990 789 789 789 789 796 798 7971991 801 769 769 769 789 789 796 7981992 797 801 788 788 788 769 789 789 7961993 798 797 801 801 791 791 791 788 769 789 7891994 796 798 797 797 801 797 797 797 791 788 769 7891995 789 796 798 819 817 821 806 806 827 817 812 808 7881996 809 809 816 821 824 822 827 813 813 834 839 832 822 817 8131997 793 814 814 835 842 845 843 816 837 842 848864 860 853 843 8371998 834 813 835 832 832 839 842 819 841 846 867 840864 861 845 857 850 8401999 835 831 811 820 842 842 849 844 849 871 868 852878 875 850 871 855 868 8602000 850 844 841 841 820 842 842 852 874 871 881 881 849 878 852 875 850 871 855 8682001 860 850 844 844 841 820 842 872 869 879 879 842 881 849 878 852 875 850 8712002 868 860 850 864 858 854 833 866 877 877 855891 893 855 895 863 892 866 8882003 885 881 874 883 873 867 863 874 874 888 842898 900 864 902 864 905 872 9012004 898 894 891 884 877 867 861 872 886 895 857889 891 836 894 858 896 858 8982005 895 891 888 885 882 875 864 865 879 888 882 859880 887 855 889 834 891 856 8942006 896 893 889 885 882 878 871 872 881 875 873 861869 876 855 883 851 885 830 8872007 890 892 889 886 883 879 876 874 867 865 862 859 868 866 858 873 852 880 849 8832008 885 887 889 883 880 876 873 860 858 854 852 869846 853 862 860 852 867 846 8742009 876 878 881 885 887 884 881 851 847 845 839 877843 850 874 857 866 864 856 8712010 878 881 883 880 882 884 881 837 835 829 833 878830 840 874 847 871 854 863 8612011 868 875 878 876 878 880 882 825 819 823 820 879818 828 876 838 872 845 869 852 859 867 874 871 873 875 878 880 816 877 826 873 836 870 843 850 857 864 858 865 867 869 872 874 810 871 820 867 830 837 844 851 848 855 862 864 866 868 870 807 867 817 827 834 841 836 843 850 857 859 861 863 865 803 812 822 829 825 832 839 846 853 855 857 859 799 809 819 819 825 832 839 846 853 855 857 799 809 809 819 825 832 839 846 853 855 799 799 809 819 825 832 839 846 853 799 809 819 825 832 839 846 799 809 819 825 832 839 799 809 819 825 832 799 809 819 825 799 809 819 799 809 799
203 Vehicle Age Table A5-5: Continued 1 2 3 4 5 6 7 8 9 10 1113 12 14 15 16 17 18 19 20
MY 2012 809 813 810 808 8062013 802 800 798 796 790 8002014 790 788 786 780 777 787 7972015 778 776 770 767 763 773 783 7932016 766 760 757 753 750 760 769 779 7892017 750 747 743 740 740 750 760 769 7792018 789 738 734 730 730 740 750 760 7692019 779 789 724 720 720 720 730 740 750 7602020 769 779 789 789 711 711 711 720 730 740 750 760 769 779 779 789 711 720 730 740 750 760 769 769 779 789 711 720 730 740 750 760 760 769 779 789 711 720 730 740 750 750 760 769 779 789 711 720 730 740 740 750 760 769 779 789 711 720 730 730 740 750 760 769 779 789 711 720 720 730 740 750 760 769 779 789 711 711 720 730 740 750 760 769 779 711 720 730 740 750 760 769 711 720 730 740 750 760 711 720 730 740 750 711 720 730 740 711 720 730 711 720 711
204 971.1 969.8 973.9 .2 992.0 984.5 948.5 (kg) (USAMP 1999; Binder 2000) LCI PARAMETERS
APPENDIX B 666.8101.4 661.8 103.4 653.2 105.2 642.3 106.1 637.3 108.0 608.3 109.1 625.5 112.9 624.1 117.5 629.8 119.3 in average US built cars in average SELECT DYNAMIC Materials use Materials Table B1: Materials 1994 1991 1992 1993 1988 1989 1990 1985 1986 1987 Regular steel, Tube, Bar and Rod steel High- and Medium-strength Stainless Steel Other Steel Iron 212.3Ferrous Total 211.1 672.0 Plastics/Composites 95.9 208.7 62.6Aluminum 98.7 98.0 (Electrical Components) Copper and Brass 207.3 63.3Lead 100.5 208.2Powdered Metal 66.2 101.2Zinc Die Casting 205.9 20.0 Casting Magnesium 101.8 67.6 195.5 20.9 Fluids and Lubricants 103.9 194.8Rubber 61.7 70.5 13.2 20.9 108.0Glass 38.6 186.7 61.0Other Materials 71.9 13.8 110.2 24.7 1020.8 22.2 185.1 38.8Total 1445.8 1018.3 61.5 111.1 75.3 1438.1 14.5 25.2 22.5 1013.6 39.0 1441.5 111.4 60.8 1000 78.7 14.1 1436.5 25.2 22.0 38.6 1424.3 61.0 80.3 14.1 20.4 1424.5 20.9 8.6 38.6 8.2 1.1 83.5 1387.5 61.9 82.6 15.4 21.3 20.4 1422.2 39.2 82.1 61.5 1428.6 8.2 16.8 17.9 19.7 39.0 1438.3 83.0 60.8 18.8 8.2 18.8 19.1 8.8 39.9 44.9 80.7 1.1 61.0 19.7 19.1 40.1 8.8 47.6 81.4 60.8 20.4 21.8 40.4 38.8 9.1 82.6 19.3 9.8 56.5 78.9 8.4 10.9 37.6 80.3 7.9 10.7 37.9 1.4 85.5 7.3 11.3 35.6 86.0 1.4 11.8 40.6 7.3 1.6 12.2 39.9 7.3 2.0 42.6 2.3
205 685.0126.0 638.2 130.2 638.9 640.0 144.7 134.0 634.6 148.8 622.8 153.8 606.9 137.5 575.0 130.2 480.6 98.0 Table B1: Continued 982.3 985.0 983.4 986.1 983.4 980.2 970.7 936.8 887.5 740.5 Materials 1995 USAMP 1996 1997 1998 1999 2000 2004 2009 2020 2009 2004 2000 1999 1998 1997 1996 USAMP Materials 1995 Iron 180.8 155.0 176.4 171.5 165.1 162.6 159.9 156.9Regular steel, Tube, Bar and Rod 148.6101.6 steel High- and Medium-strength 132.9 Stainless Steel 110.4 106.6Other Steel 111.1 111.4Iron 180.8 112.7 110.7Ferrous Total 634.1 155.0Plastics/Composites 111.8 121.5 115.8 126.8 85.0Aluminum 1479.463.364.0 176.443.144.0 127.0 106.8 143.0 (Electrical Components) Copper and Brass 1485.1 171.5 Lead 13.0 1490.5 107.8 111.1 96.3Powdered Metal 1464.6 65.3 109.8 19.7 1425.1Zinc Die Casting 89.1 44.7 1212.1 Casting Magnesium 65.3 44.7 Fluids and Lubricants 93.4 26.5Rubber 61.7 20.9 65.3 44.7 20.4Glass 41.5 Other Materials 58.8 19.7 68.0 42.6 19.0 21.1 Total 1455.1 42.0 63.5 21.1 20.9 1532.0 42.6 1467.8 62.8 21.5 20.6 1473.3 43.8 12.7 17.5 22.2 20.9 7.3 2.3 86.2 22.9 16.3 20.8 74.0 15.2 24.0 0.3 19.8 89.6 13.4 11.3 21.1 20.4 7.0 44.7 2.5 89.6 10.2 20.0 14.1 6.4 89.6 15.0 14.4 15.9 2.7 6.1 88.0 15.6 13.7 2.9 45.1 89.8 16.3 5.4 13.1 46.3 3.2 89.8 15.7 5.2 40.4 3.6 89.8 15.7 46.3 6.2 64.7 13.4 3.2 49.9 6.3 3.2 49.9 7.0 49.9 5.0 45.1
206
Table B2: Energy intensity indices between the calendar years 1985 and 2020 using 1995 as a baseline year
Materials Production Manufacturing Use/End-of- Year Average Ferrous /Maintenance Life Aluminum Plastics (Other Materials Materials) 1985 1.212 1.098 1.000 1.103 0.932 1.000 1986 1.201 1.079 1.000 1.093 0.928 1.000 1987 1.189 1.059 1.000 1.083 0.924 1.000 1988 1.178 1.040 1.000 1.073 0.920 1.000 1989 1.142 1.033 1.000 1.059 0.929 1.000 1990 1.107 1.026 1.000 1.045 0.939 1.000 1991 1.072 1.020 1.000 1.031 0.948 1.000 1992 1.059 1.018 1.000 1.026 0.968 1.000 1993 1.047 1.016 1.000 1.021 0.988 1.000 1994 1.034 1.014 1.000 1.016 1.007 1.000 1995 1.000 1.000 1.000 1.000 1.000 1.000 1996 0.966 0.986 1.000 0.984 0.999 1.000 1997 0.932 0.973 1.000 0.968 0.977 1.000 1998 0.899 0.959 1.000 0.953 0.976 1.000 1999 0.899 0.957 1.000 0.952 0.964 1.026 2000 0.879 0.950 0.995 0.941 0.950 1.032 2001 0.861 0.941 0.992 0.931 0.949 1.058 2002 0.842 0.933 0.989 0.921 0.930 1.055 2003 0.828 0.924 0.986 0.913 0.920 1.067
2004 0.816 0.916 0.981 0.904 0.910 1.067 2005 0.802 0.909 0.978 0.897 0.891 1.067 2006 0.791 0.902 0.976 0.889 0.875 1.084 2007 0.779 0.895 0.973 0.882 0.862 1.096 2008 0.770 0.888 0.970 0.876 0.846 1.088 2009 0.761 0.882 0.968 0.870 0.833 1.085 2010 0.751 0.875 0.965 0.864 0.820 1.080 2011 0.743 0.869 0.962 0.858 0.807 1.077 2012 0.733 0.864 0.959 0.852 0.794 1.069 2013 0.726 0.858 0.959 0.848 0.782 1.075 2014 0.718 0.852 0.957 0.842 0.769 1.071 2015 0.712 0.847 0.954 0.838 0.756 1.069 2016 0.705 0.842 0.951 0.833 0.745 1.066 2017 0.699 0.838 0.949 0.829 0.733 1.058 2018 0.694 0.834 0.949 0.825 0.720 1.054 2019 0.687 0.830 0.946 0.821 0.708 1.048 2020 0.682 0.826 0.943 0.817 0.697 1.044
207
Table B3: Replacement parts and fluids, quantity used in lifetime of a car Replacement Part or Fluid Average Unit/ Quantity Lifetime Brake fluid 3 Liter Engine coolant fluid 22.2 Liter Engine oil 78.1 Liter Transaxle fluid 28 Liter Windshield cleaner fluid 44 Liter Air filter 4.3 pcs Battery 1.7 pcs Brake pads front 1 pcs Brake pads rear 1 pcs Drive belt 2 pcs Lamp Bulbs 3.5 pcs Muffler, exhaust pipe 1 pcs Oil filter 15.7 pcs PCV-valve 2 pcs
Shock absorbers 2 pcs Spark plugs 16 pcs Tires 2 pcs Transaxle fluid filter 1 pcs Windshield 1 pcs Windshield wiper blades 18.7 pcs Source: (USAMP 1999)
208 APPENDIX C
FLEET OPTIMIZATION MODEL
Flow chart
Start
Read data (VMT, fleet, dynamic LCIs)
Initialize optimal (2001) fleet dist. and minimum Burden (Min) 209
New prod. = 100
Get new (2001) fleet New prod. = Yes dist. and Burden No New Burden < Min ? New prod. + 1 prod.<=100+UB ? based on new prod. and fixed fleet VMT
Yes No Print optimal (2001) Update optimal fleet dist. and (2001) fleet dist. and minimum Burden minimum Burden
End
Source code
#include
#define Horizon 20 #define Life 20 #define M 900000000 #define N 999999999 #define B 1981 /*Beginning Year of the MY considered*/ #define year 2001 /*Simulation start year*/ #define Ncar 100 /*Baseline number of new cars produced in a year*/ #define LB 100 /*Lower bound of reduced production*/ #define UB 500 /*Upper bound of additional production*/
#define VMTFile "AnnualVMT.dat" #define CarFile "Fleet.dat" #define HazardFile "HR.dat" #define MfgFile "MfgBC2.dat" #define UseFile "UseBC2.dat" #define EOLFile "EOLBC2.dat"
/*Function to read data values*/ void ReadValues(double *VMT, double *Cars, double *HR, double *MfgB, double *UseB[], double *EOLB[]);
/*Function to calculate total VMT of fleet*/ double GetTVMT(double *Cars, double *VMT);
/*Function to update fleet distribution to meet the total VMT requirement*/ double* Cupdate(double NewCar, double *Cars, double *VMT, double *HR);
/*Function to calculate emissions*/ double calB(int y, double *OCars, double *NCars, double *VMT, double *HR, double *MfgB, double *UseB[], double *EOLB[]);
/*Function to calculate use phase emissions*/ double calUseB(int y, double *NCars, double *VMT, double *UseB[]);
/*Function to calculate end-of-life emissions*/ double calEOLB(int y, double *OCars, double *NCars, double *HR, double *EOLB[]);
/*Function to calculate manufacturing emissions*/ double calMfgB(int y, double *NCar, double *MfgB);
/*Round function to get integer values*/ double round(double number); int main(void)
210
{ int i, i1, i2, i3, j, k, l, m;/*variables for indexes*/ double Min = M; /*Initial value of Min=M(big number)*/ int optMfg = Ncar; /*Variable for optimal prod. of new car*/ double burden, Sburden; double TOVMT, TNVMT;
/*Arrays for VMT of survived car, Current cars, Survival rate of car, Hazard rate of car, Mfg. burden, Use burden, and Eol burden*/ double *VMT, *Cars, *HR, *MfgB; double *UseB[Horizon+1], *EOLB[Horizon+1];
/*Arrays for New fleet VMT, Old fleet VMT, and Opt Fleet composition*/ double *NCar, *XCar, *OptFleet, *OptVMT;
/*Allocating memories for arrays. Horizon+1 places for each of the array*/ VMT = (double *) calloc(Horizon+1, sizeof(double)); Cars = (double *) calloc(Horizon+1, sizeof(double)); HR = (double *) calloc(Horizon+1, sizeof(double)); MfgB = (double *) calloc(Horizon+1, sizeof(double));
for (i=0; i<=Horizon; i++) { UseB[i] = (double *) calloc(Life+1, sizeof(double)); EOLB[i] = (double *) calloc(Life+1, sizeof(double)); }
ReadValues(VMT, Cars, HR, MfgB, UseB, EOLB);
NCar = (double *) calloc(Horizon+1, sizeof(double)); XCar = (double *) calloc(Horizon+1, sizeof(double)); OptFleet = (double *) calloc(Horizon+1, sizeof(double));
OptVMT = (double *) calloc(Horizon+1, sizeof(double));
cout<<'\n'<<"The original Fleet at year " < for(i1=1; i1<=Horizon; i1++) { cout<<*(Cars+i1)<<'\n'; cout<<'\n'<<"The original VMT at year " < for(i2=1; i2<=Horizon; i2++) { cout<<*(VMT+i2)<<'\n'; } cout<<'\n'<<"The original Fleet VMT at year " < for(i3=1; i3<=Horizon; i3++) { 211 cout<<(*(VMT+i3))*(*(Cars+i3))<<'\n'; } TOVMT = GetTVMT(Cars, VMT); XCar=Cupdate(Ncar, Cars, VMT, HR); Sburden = calB(year, Cars, XCar, VMT, HR, MfgB, UseB, EOLB); /*Determine optimal new production and new fleet distribution. The range of new cars encompasses between ‘ncar-LB’ and ‘ncar+UB’ */ for(j=Ncar-LB; j<=Ncar+UB; j++) { NCar=Cupdate(j, Cars, VMT, HR); TNVMT=GetTVMT(NCar, VMT); /*Calculate environmental burden from the mfg of new cars, use of the new fleet, and scrap of old fleet*/ if (TNVMT else { burden = calB(year, Cars, NCar, VMT, HR, MfgB, UseB, EOLB); } if(burden { Min = burden; optMfg = j; for(k=1; k<=Horizon; k++) { *(OptFleet+k) = *(NCar+k); *(OptVMT+k) = (*(NCar+k))*(*(VMT+k)); } } } cout<<'\n'<<"Before optimization at year "< cout<<"The Status quo Burden: " < cout<<'\n'<<"After optimization at year "< cout<<'\n'<<"The optimum new Mfg: " < cout<<"The Minimum Burden: " < cout<<"Savings: " << ((Sburden-Min)/Sburden)*100 <<" %"<<'\n'; 212 cout<<'\n'<<"The opt Fleet: "<<'\n'; for(l=1 ;l<=Horizon ;l++) { cout< cout<<'\n'<<"The opt Fleet VMT: "<<'\n'; for(m=1 ;m<=Horizon ;m++) { cout< return 0; } /****Function to read necessary arrays******/ void ReadValues(double *VMT, double *Cars, double *HR, double *MfgB, double *UseB[], double *EOLB[]) { int i; int j; ifstream VMTIn, CarsIn, MfgIn, UseIn, EOLIn, HRIn; VMTIn.open(VMTFile, ios::in); CarsIn.open(CarFile, ios::in); HRIn.open(HazardFile, ios::in); MfgIn.open(MfgFile, ios::in); UseIn.open(UseFile, ios::in); EOLIn.open(EOLFile, ios::in); if (VMTIn.fail() ||CarsIn.fail() ||MfgIn.fail() || UseIn.fail()|| EOLIn.fail()) { cerr <<"Problem Opening File\n\n"; exit(-1); } for (i=1; i<=Horizon; i++) { VMTIn >> (*(VMT+i)); CarsIn >> (*(Cars+i)); HRIn >> (*(HR+i)); MfgIn >> (*(MfgB+i)); for (j=1; j<=Life; j++) { UseIn >> (*(UseB+i))[j]; EOLIn >> (*(EOLB+i))[j]; } } 213 } /********Function to get total VMT*********************/ double GetTVMT(double *Cars, double *VMT) { int i; double sum = 0; for(i=1; i<=Horizon; i++) { sum=sum+(*(VMT+i))*(*(Cars+i)); } return sum; } /***Function to get optimal fleet distribution based on new car production and total fleet VMT************/ double* Cupdate(double NewCar, double *Cars, double *VMT, double *HR) { int i,j,k; /*Index Variable*/ double OldTV, NewTV; /*Old and New total VMT*/ double *Xvmt; /*Temporary VMT arrays*/ double *NCar, *XCar; NCar = (double *)calloc(Horizon+1, sizeof(double)); XCar = (double *)calloc(Horizon+1, sizeof(double)); *(NCar+1) = NewCar; /*For the 1st year*/ for(i=2; i<=Horizon; i++) /*From the 2nd year */ { *(NCar+i)= (*(Cars+i-1))*(1-(*(HR+i-1))); } OldTV=GetTVMT(Cars, VMT); NewTV=GetTVMT(NCar, VMT); if(NewTV<=OldTV) { return NCar; } else /*Return minimum New Car */ { j = Horizon; for( ; ; ) { for(k=1;k<=Horizon; k++) 214 { *(XCar+k)=*(NCar+k); } if(*(NCar+j)>0) { *(NCar+j)= *(NCar+j)-1; } else { *(NCar+j)= 0; j=j-1; } NewTV=GetTVMT(NCar, VMT); if(NewTV < OldTV) break; } return XCar; } } /************Functions to calculate emissions **********************/ double calB(int y, double *OCars, double *NCars, double *VMT, double *HR, double *MfgB, double *UseB[], double *EOLB[]) { double b; b=calMfgB(y, NCars, MfgB) +calUseB(y, NCars, VMT, UseB) +calEOLB(y, OCars, NCars, HR, EOLB); return b; } double calUseB(int y, double *NCar, double *VMT, double *UseB[]) { int i; double temp; double sum = 0; for(i=B+1; i<=y; i++) { temp=(*(NCar+y-i+1))*(*(VMT+y-i+1))*((*(UseB+i-B))[y-i+1]); sum=sum+temp; } return sum; 215 } double calEOLB(int y, double *OCars, double *NCars, double *HR, double *EOLB[]) { int i,j; double OldV, NewV; double sum1 = 0; double sum2 = 0; double temp1, temp2; temp1=(*(OCars+y-B))*((*(EOLB+1))[y-B]); sum1=sum1+temp1; j=Horizon-1; for(i=1; i<=j; i++) { temp1=((*(OCars+i))*(1-(*(HR+i)))-(*(NCars+i+1))) *((*(EOLB+y-B-i+1))[i]); sum1=sum1+temp1; temp2=(*(OCars+i))*(*(HR+i))*((*(EOLB+y-B-i+1))[i]); sum2=sum2+temp2; } return sum1+sum2; } double calMfgB(int y, double *NCar, double *MfgB) { return (*(NCar+1))*(*(MfgB+y-year+1)); } double round(double number) { return floor(number+0.5); } 216 APPENDIX D BREAKDOWN OF HIGH-EMITTERS BASED ON MODEL YEARS Table D1: Breakdown of CO high-emitters during the 2000 IM cycle based on outcomes of initial 2002 tests Failed pollutant in initial 2002 test (cars failed) i Repeat- g h Repeat Model d e f reason Total a b c HC/ failure years HC/ CO/ HC/ Pass failure (=h+i) HC CO NOx CO/ (=a+b+c+ CO NOx NOx d+e+f+g) (=b+d+e+g) NOx 1981 0 39 3 22 3 0 0 62 67 64 129 1982 0 34 8 18 3 0 0 79 63 55 142 1983 0 43 11 26 1 0 0 84 81 70 165 217 1984 0 49 18 39 3 0 2 134 111 93 245 1985 0 77 14 40 4 0 2 168 137 123 305 1986 0 37 10 24 2 1 6 136 80 69 216 1987 1 37 14 33 6 0 6 132 97 82 229 1988 1 43 15 27 9 2 5 115 102 84 217 1989 0 74 18 33 10 5 6 197 146 123 343 1990 0 50 9 45 6 3 9 174 122 110 296 1991 1 30 11 24 13 4 4 160 87 71 247 1992 0 13 8 16 4 2 4 157 47 37 204 1993 1 11 9 25 3 2 4 110 55 43 165 1994 0 4 4 2 0 0 2 55 12 8 67 1995 0 6 5 1 0 1 2 106 15 9 121 Total 4 547 157 375 67 20 52 1869 1222 1041 3091 Table D2: Breakdown of NOx high-emitters during the 2000 IM cycle based on outcomes of initial 2002 tests Failed pollutant in initial 2002 test (cars failed) i Repeat- g h Repeat Model d e f reason Total a b c HC/ failure years HC/ CO/ HC/ Pass failure (=h+i) HC CO NOx CO/ (=a+b+c+ CO NOx NOx d+e+f+g) (=c+e+f+g) NOx 1981 0 8 14 3 2 0 1 50 28 17 78 1982 1 11 10 10 1 1 0 65 34 12 99 1983 0 12 34 19 1 0 0 111 66 35 177 1984 0 20 58 17 6 2 0 175 103 66 278 218 1985 0 21 62 21 6 0 2 272 112 70 384 1986 4 23 143 49 13 10 26 483 268 192 751 1987 2 27 147 40 17 7 14 503 254 185 757 1988 2 25 160 38 32 13 24 492 294 229 786 1989 2 29 148 38 16 16 15 525 264 195 789 1990 1 16 102 30 19 14 24 388 206 159 594 1991 3 11 94 23 18 17 18 335 184 147 519 1992 0 4 68 9 8 11 14 241 114 101 355 1993 2 3 93 14 11 12 9 316 144 125 460 1994 1 4 95 13 7 19 9 318 148 130 466 1995 2 2 63 7 3 13 10 389 100 89 489 Total 20 216 1291 331 160 135 166 4663 2319 1752 6982 Table D3: Breakdown of HC/CO high-emitters during the 2000 IM cycle based on outcomes of initial 2002 tests Failed pollutant in initial 2002 test (cars failed) i Repeat- g h Repeat Model d e f reason Total a b c HC/ failure years HC/ CO/ HC/ Pass failure (=h+i) HC CO NOx CO/ (=a+b+c+ CO NOx NOx d+e+f+g) (=d+g) NOx 1981 0 12 5 14 1 0 0 42 32 14 74 1982 0 15 1 27 2 0 0 38 45 27 83 1983 1 26 7 43 1 2 2 53 82 45 135 1984 0 36 7 54 5 0 2 87 104 56 191 219 1985 0 41 7 72 3 0 4 116 127 76 243 1986 1 27 19 80 6 3 5 137 141 85 278 1987 1 38 18 78 4 6 14 163 159 92 322 1988 0 22 14 53 7 2 9 141 107 62 248 1989 2 27 16 35 7 4 9 122 100 44 222 1990 2 33 15 85 3 10 20 178 168 105 346 1991 2 10 7 50 8 6 11 179 94 61 273 1992 3 9 11 43 6 2 7 152 81 50 233 1993 2 10 6 29 1 4 8 120 60 37 180 1994 0 3 8 5 1 5 6 86 28 11 114 1995 1 3 13 10 0 3 2 146 32 12 178 Total 15 312 154 678 55 47 99 1760 1360 777 3120 Table D4: Breakdown of HC/CO/NOx high-emitters during the 2000 IM cycle based on outcomes of initial 2002 tests Failed pollutant in initial 2002 test (cars failed) i Repeat- Repeat Model g h reason Total d e f HC/ failure years a b c HC/ CO/ HC/ Pass failure (=h+i) HC CO NOx CO/ (=a+b+c+ CO NOx NOx d+e+f+g) (=g) NOx 1981 0 2 0 0 0 0 0 3 2 0 5 1982 0 3 0 5 1 0 0 2 9 0 11 1983 0 4 4 5 2 0 2 9 17 2 26 1984 0 3 2 3 0 0 0 8 8 0 16 1985 1 5 3 3 0 0 4 16 16 4 32 220 1986 1 9 19 33 7 6 14 63 89 14 152 1987 0 9 20 16 7 2 8 52 62 8 114 1988 0 3 11 15 4 3 7 44 43 7 87 1989 1 6 10 11 3 2 7 57 40 7 97 1990 2 5 8 18 7 9 19 101 68 19 169 1991 2 4 16 18 5 3 13 63 61 13 124 1992 2 3 6 10 3 3 12 51 39 12 90 1993 0 0 2 5 1 7 8 23 23 8 46 1994 3 0 12 5 0 3 10 36 33 10 69 1995 2 1 3 0 0 4 5 42 15 5 57 Total 14 57 116 147 40 42 109 570 525 109 1095 BIBLIOGRAPHY 221 BIBLIOGRAPHY AISI (1998) Steel Industry Technology Roadmap. 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