Motor Vehicle Loans Emission Factors Database Methodology Database Methodology

Economic Activity-based Emission Factors

Motor Vehicle Loans Emission Factors Database methodology Database methodology

April 2020 April 2020

Motor Vehicle Loans Emission Factors – Database Methodology

Table of Contents

1. Overview ...... 4 1.1. Definitions ...... 5 1.1.1 Emission factor of a vehicle...... 5 1.1.2 Emission rate of a vehicle ...... 5 1.1.3 Propulsion types ...... 5 1.1.4 Scopes 1 and 2 emissions ...... 5 1.2. Methodology tree ...... 6 2. Detailed methodology ...... 7 2.1. Primary method: make-and-model data ...... 7 2.1.1. Equation ...... 7 2.1.1. Country-source matrix...... 8 2.2. Secondary method: average vehicle type data ...... 10 2.2.1. Equations...... 10 2.2.2. Countries matrix ...... 11 3. Sources and assumptions ...... 12 3.1. Make-and-model data ...... 12 3.1.1. U.S. EPA’s FTP ...... 12 3.1.2. EU EEA’s WLTP ...... 12 3.1.3. Other regions ...... 13 3.2. National and regional data ...... 13 3.2.1. ICCT Transportation Roadmap ...... 13 3.2.2. ITF-OECD Transportation performance indicators ...... 14 3.2.3. Electricity emission rates ...... 14 3.2.4. State- and province-level data (USA & CAN) ...... 14

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List of acronyms

BTU British Thermal Units CO2 (gCO2, tCO2) Carbon dioxide (gram of carbon dioxide, ton of carbon dioxide) EEA European Environment Agency EF Emission factor ER Emission rate U.S. EPA U.S. Environmental Protection Agency EU European Union FTP EPA Federal Test Procedure GHG Greenhouse gas ICCT International Council on Clean Transportation ICE Internal combustion engine IEA International Energy Agency ITF International Transport Forum km kilometer mi mile mpg mile per gallon NEDC New European Driving Cycle OECD Organization for Economic Co-operation and Development CAN OEE Canadian Office of Energy Efficiency PCAF Partnership for Carbon Accounting Financials TTW tank-to-wheel TP vehicle testing procedure UF utility factor Wh (kWh) watt-hour (kilowatt-hour) WLTP Worldwide harmonized Light vehicles Test Procedure WTT well-to-tank

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1. Overview In order to enable banks and other financial institutions to calculate the greenhouse gas (GHG) emissions associated with their portfolios of motor vehicle loans, the Partnership for Carbon Accounting Financials (PCAF) has built a database of motor vehicle emission factors for 193 different countries1, and for all the individual states and provinces of the United States and Canada.

If a bank gave respectively {푛1, 푛2, … , 푛푘} loans of vehicles {푉1, 푉2, … , 푉푘} in countries { } ( ) 퐶1, 퐶2, … , 퐶푘 , it can use the PCAF database to find the annual emission factors 퐸퐹퐶푖 푉푖 associated with each one of the loan and calculate E, the total quantity of GHG emissions per year that are emitted by all vehicles being financed through these loans: 푘 ( ) 퐸 = ∑[ 퐸퐹퐶푖 푉푖 × 푛푖] 푖=1 For example, if a bank has given loans for two Toyota Yaris in Japan, one Toyota Yaris in China, three Audi TT in Korea, five heavy-duty trucks in China, and one motorcycle in Singapore, then {푛1, 푛2, … , 푛푘} = {2,1,3,5,1}, {푉1, 푉2, … , 푉푘} = {Toyota Yaris, Toyota Yaris, Audit TT, Heavy-duty truck, Motorcycle}, {퐶1, 퐶2, … , 퐶푘} = {Japan, China, Korea, China, Singapore}.

The PCAF motor vehicle loans emission factors database plays the role of the function 퐸퐹 which returns 퐸퐹: (푉, 퐶) → 퐸퐹퐶(푉). The following pages explain the methodology used to develop this database. The next paragraph defines the technical terms used throughout this document and in the database itself. §1.2 provides an overview of the two types of emission factors found in the database and their associated methods: • The Primary method calculates emission factors at the make-and-model level. They are only available for passenger cars but should be used by carbon accounting financials whenever possible. • The Secondary method calculates average emission factors for broad vehicle types – passenger cars, buses, motorcycles, light commercial trucks and medium/heavy commercial trucks. Part 2 dives into the details of these two methods. Part 3 lists the sources used to develop the database and explains the important assumptions made regarding each of them.

1 The 193 United Nations member states.

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1.1. Definitions This paragraph defines the important technical terms used throughout this document as well as in the PCAF database. The definition of these notions is adapted to the PCAF motor vehicle loans context – they might have different meaning elsewhere.

1.1.1 Emission factor of a vehicle The emission factor (EF) of a vehicle in the PCAF database is the average mass of carbon dioxide, in metric tons of CO2, emitted by this vehicle in one year (tCO2/vehicle-year).

1.1.2 Emission rate of a vehicle The emission rate (ER) of a vehicle in the PCAF database is the average mass of carbon dioxide, in grams of CO2, emitted by this vehicle when it drives one mile (gCO2/mi).

1.1.3 Propulsion types Thermal propulsion A vehicle’s propulsion is thermal when it uses an internal combustion engine (ICE) only, burning fuel (gasoline, diesel, petroleum gas, etc.) on the road. • Fuel efficiency (FE): for a thermal vehicle, efficiency is measured as the volume of fuel combusted to cover a certain distance (e.g. liters/100 km) or equivalently the distance covered using a certain fuel quantity (e.g. mpg). Electric propulsion A vehicle’s propulsion is electric when it uses an electric motor only, with no fuel combustion. • Electric efficiency (EE): for an electric vehicle, efficiency is measured as the electric energy used to cover a certain distance (e.g. kWh/100 mi). Hybrid propulsion A hybrid vehicle uses both an ICE and an electric motor. The utility factor (UF, in %) of a hybrid vehicle is the average percentage of its total miles that it covers using its electric motor, the rest being covered by burning fuel (UF = 0% for thermal vehicles, 100% for electric vehicles). • Hybrid efficiency: a hybrid vehicle has both a fuel efficiency (e.g. mpg) and an electric efficiency (e.g. Wh/km). Using the vehicle’s utility factor and the heat content of the burnt fuel, one can calculate the average energy efficiency of the vehicle (e.g. in BTU/km).

1.1.4 Scopes 1 and 2 emissions In standard carbon accounting (as defined by the Greenhouse Gas Protocol), the GHG emissions of any entity can be broken down into “scopes”, depending on how close these emissions are related to the entity: Scope 1 include all emissions directly produced by the entity (direct emissions) and Scope 2 emissions include all emissions produced by the generation of the secondary energy consumed by the entity (indirect emissions). When the entity is a vehicle, these two Scopes can be defined as follows:

Scope 1 emissions of a thermal or hybrid vehicle: Direct GHG emissions (mainly CO2) released through the tailpipe of the vehicle following the combustion of fuel in its ICE. Scope 1 vehicle emissions are also called “tank-to-wheel” (TTW) emissions. They occur on the road. Electric vehicles have no Scope 1 emissions.

Scope 2 emissions of an electric or hybrid vehicle:

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Indirect GHG emissions (mainly CO2) released during the generation of the electricity that is used by the vehicle’s electric motor. Scope 2 vehicle emissions belong to the “well-to-tank” (WTT) category. They do not occur on the road but in the power plant that generated the electricity, upstream of the driving. Thermal vehicles have no Scope 2 emissions.

1.2. Methodology tree The PCAF database contains two types of emission factors: 1) Emission factor for a specific make and model (e.g. Ford Focus) 2) Emission factor for a broad vehicle type (e.g. motorcycles). Both types of emission factor are country-specific. For the United States and Canada only, state- and province-specific emission factors can also be found in the PCAF database. The make-and-model granularity is currently only available for passenger cars, or light-duty vehicles. It covers thermal, hybrid and electric vehicles. Since a better granularity always improves the accuracy of a carbon footprint, carbon accounting financials should use make-and-model emission factors to calculate their motor vehicle loans emissions whenever this level of information is available. The method used by PCAF to calculate make-and-model emission factors is detailed in §2.1. For other types of vehicles (buses, motorcycles, light/medium/heavy-duty trucks) and for light-duty vehicles of which the make and model are unknown, the PCAF database provides average emission factors. These emission factors are averaged over the entire fleet of vehicles in use in the country of the loan, including all propulsion types (thermal, hybrid, electric). However, because of the current overwhelming majority of thermal engines in all of the world’s vehicle fleets, banks should not use these average emission factors for a vehicle that they know is hybrid or electric. Rather, they should look up the make and model of the electric/hybrid vehicle in order to use its make-and-model- specific emission factor. The method used by PCAF to calculate average emission factors is detailed in §2.2. Figure 1 provides an overview of which method is used depending on the type of vehicle and the level of data available.

Figure 1. Methodology tree

Primary §2.1 Are the make method and model of the vehicle known? Secondary What type is method the vehicle? §2.2 Secondary method

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2. Detailed methodology

2.1. Primary method: make-and-model data

2.1.1. Equation The calculation of the primary method is summarized by the following equation: ̅̅̅̅̅̅̅̅̅̅̅̅̅̅ ̅̅̅̅̅̅̅̅̅ ̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅ (1) 퐸퐹푅(푉푀,푚) = [ 푈퐹푇푃(푅)(푉푀,푚) × 퐸퐸푇푃(푅)(푉푀,푚) × 퐸퐹푅(푒) + (1 − 푈퐹푇푃(푅)(푉푀,푚)) × 퐸푅푇푃(푅)(푉푀,푚) ] × 퐷푅(푉푇(푀,푚)) Where: ̅̅̅̅̅̅̅̅̅̅̅̅̅̅ • 퐸퐹푅(푉푀,푚) is the average emission factor (̅퐸퐹̅̅̅), in region R, of a vehicle V of make M and model m (in gCO2e/vehicle-year) • 푈퐹푇푃(푅)(푉푀,푚) is the utility factor of a vehicle V of make M and model m, obtained through the vehicle testing procedure TP(R) (in %). (푈퐹 = 0% for thermal vehicles, 100% for electric vehicles) • 퐸퐸푇푃(푅)(푉푀,푚) is the electric efficiency of a vehicle V of make M and model m, obtained through the vehicle testing procedure TP(R) (in kWh/mi) ̅̅̅̅̅̅̅̅̅ • 퐸퐹푅(푒) is the average emission factor of grid electricity (푒) in region R (in gCO2/kWh) • 퐸푅푇푃(푅)(푉푀,푚) is the emission rate of a vehicle V of make M and model m, obtained through the vehicle testing procedure TP(R) (in gCO2/mi) ̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅ • 퐷푅(푉푇(푀,푚)) is the average distance (퐷̅), in region R, driven in one year by a vehicle V of type T , which encompasses the make M and model m (in mi/vehicle-year). Currently, T is always Passenger cars, since make-and-model data is not available for other types of vehicles. For other types of vehicles, the secondary method is used (see §2.2).

For example, if R = Illinois, M = Toyota and m = Prius Plug-in Hybrid, equation (1) reads that the average emission factor of a Toyota Prius Plug-in Hybrid in Illinois is equal to the average distance it drives each year multiplied by the sum of, on one hand, the product of its utility factor (% of miles driven using the electric motor) times the efficiency of the motor (kWh/mi) times the carbon intensity of the electricity in Illinois (gCO2/kWh), and on the other hand the product of 1 minus its utility factor (% of miles driven using the ICE) times the emission rate of the engine (in gCO2/mi).

For some countries, data on average distance per vehicle-year is not available. For such a country C, ̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅ ̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅ the following assumption is made: 퐷퐶(푉푇(푀,푚)) = 퐷푅(퐶)(푉푇(푀,푚)) where R(C) is the smallest region that encompasses C and that has data available on distance per vehicle-year. For example, because average distance per vehicle-year data is not available for Uganda, the average distance per vehicle- year of Africa was used.

Note on TP(R) – There are different vehicle testing procedures around the world. In the PCAF database, the testing procedures used are as follow: • if 푅 ∈ European Union, then 푇푃(푅) = 푊퐿푇푃 • else 푇푃(푅) = 퐹푇푃 i.e. for vehicle loans in the 28 countries of the European Union (pre-Brexit), the PCAF database uses results from the European Environment Agency (EEA)’s Worldwide Harmonized Light Vehicles Test Procedure (WLTP). For the rest of the world, PCAF uses results from the U.S. Environmental Protection Agency (EPA)'s Federal Test Procedure (FTP). See §3.1.3 for the justification of this hypothesis.

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2.1.1. Country-source matrix Table 1 lists the data sources used for each term of equation (1), for every country in the PCAF database. Please refer to §3 for more details on each source. The aggregation of these various sources yields different levels of accuracy in the calculation of emission factors. The accuracy level by country is also shown below (color-coding matches the maps that follow the table). Table 1. Breakdown of equation (1) by source for all countries 푈퐹 ; Method ̅̅̅̅ 푇푃(푅) ̅̅̅̅̅̅̅̅̅ Countries 퐷푅 퐸퐹푅(푒) accuracy 퐸퐸푇푃(푅); 퐸푅푇푃(푅) • ICCT for the country • IEA for the country United States • CarInsurance for states • eGRID for states Very High • ICCT for the country • IEA for the country Canada • CAN OEE for provinces U.S. EPA’s FTP • Green-e for provinces Australia, Brazil, China, India, Japan, Mexico, South Korea, Russia High Country-level ICCT data Iceland, Israel, New Zealand, Norway, Switzerland, Turkey Austria, Belgium, Croatia, Czech Republic, Estonia, Finland, France, Germany, Hungary, Ireland, Medium Country-level ITF data Luxembourg, Netherlands, Slovenia, Sweden, United Kingdom EU EEA’s WLTP Bulgaria, Cyprus, Denmark, Greece, Italy, Latvia, Regional ICCT data: Lithuania, Malta, Poland, Portugal, Romania, Slovakia, Spain European Union Algeria, Angola, Benin, Botswana, Burkina Faso, Burundi, Cape Verde, Cameroon, Central African Republic, Chad, Comoros, Congo, Republic of the, Congo, Democratic Republic of the, Cote d'Ivoire, Djibouti, Equatorial Guinea, Eritrea, Eswatini, Ethiopia, Gabon, Gambia, Ghana, Guinea, Guinea- Regional ICCT data: Bissau, Kenya, Lesotho, Liberia, Libya, Madagascar, Malawi, Mali, Mauritania, Mauritius, Morocco, Africa Mozambique, Namibia, Niger, Nigeria, Rwanda, Sao Tome and Principe, Senegal, Seychelles, Sierra Leone, Somalia, South Africa, South Sudan, Sudan, Tanzania, Togo, Tunisia, Uganda, Zambia, Zimbabwe Afghanistan, Bangladesh, Bhutan, Brunei, IEA Cambodia, Fiji, Indonesia, Kazakhstan, Kiribati, North Korea, Kyrgyzstan, Laos, Malaysia, Maldives, Marshall Islands, Micronesia, Mongolia, Myanmar, Region ICCT data: Nauru, Nepal, Pakistan, Palau, Papua New Guinea, Low Philippines, Samoa, Singapore, Solomon Islands, Sri Other Asia-Pacific Lanka, Tajikistan, Thailand, Timor-Leste, Tonga, U.S. EPA’s FTP Turkmenistan, Tuvalu, Uzbekistan, Vanuatu, Vietnam Albania, Andorra, Armenia, Azerbaijan, Belarus, Bosnia and Herzegovina, Georgia, Liechtenstein, Regional ICCT data: Moldova, Monaco, Montenegro, North Other Europe Macedonia, San Marino, Serbia, Ukraine Antigua and Barbuda, Argentina, Bahamas, Barbados, Belize, Bolivia, Chile, Colombia, Costa Rica, Cuba, Dominica, Dominican Republic, Ecuador, El Salvador, Grenada, Guatemala, Regional ICCT data: Guyana, Haiti, Honduras, Jamaica, Nicaragua, Panama, Paraguay, Peru, Saint Kitts and Nevis, Other Latin America Saint Lucia, Saint Vincent and the Grenadines, Suriname, Trinidad and Tobago, Uruguay, Venezuela Bahrain, Egypt, Iran, Iraq, Jordan, Kuwait, Lebanon, Regional ICCT data: Oman, Qatar, Saudi Arabia, Syria, United Arab Emirates, Yemen Middle-East

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Figure 2 and Figure 3 help visualize the table above.

Figure 2. 푫̅̅̅푹̅ data source map

Figure 3. Vehicle testing procedure map

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2.2. Secondary method: average vehicle type data

2.2.1. Equations The calculation of the secondary method is summarized by the following equation:

̅̅̅̅̅̅̅̅̅̅̅ ∑푅,푇 퐸(푉) (2) 퐸퐹푅(푉푇) = |푉|푅,푇 Where: ̅̅̅̅̅̅̅̅̅̅̅ • 퐸퐹푅(푉푇) is the average emission factor (̅퐸퐹̅̅̅), in region R, of a vehicle of type T • ∑푅,푇 퐸(푉) is the sum of the CO2 emissions of all vehicles of type T in region R • |푉|푅,푇 is the total number (or cardinal, or stock) of vehicles of type T in region R

For example, if R = China and T = heavy-duty trucks, equation (2) reads that the average emission factor of a heavy-duty truck in China is calculated by dividing the total amount of CO2 emissions from all heavy-duty trucks in China by the total number of heavy-duty trucks in China.

However, data on ∑푅,푇 퐸(푉) and |푉|푅,푇 is only available in a select number of countries and world regions (see §2.2.2). For the countries where it is not available, but where average distance driven per vehicle-year is known, an additional calculation step is taken: ̅̅̅̅̅̅̅̅̅̅ ̅̅̅̅̅̅̅̅̅̅ ̅̅̅̅̅̅̅̅̅̅̅ ̅̅̅̅̅̅̅̅̅̅̅̅̅̅ 퐷퐶(푉푇) ∑푅(퐶),푇 퐸(푉) 퐷퐶(푉푇) (3) 퐸퐹퐶(푉푇) = 퐸퐹푅(퐶)(푉푇) × = × 퐷̅̅̅푅̅̅(̅퐶̅)̅(̅̅푉̅̅푇̅̅) |푉|푅(퐶),푇 퐷̅̅̅푅̅̅(̅퐶̅)̅(̅̅푉̅̅푇̅̅) With: • 푅(퐶) is the region that encompasses country 퐶 and in which data on ∑푅,푇 퐸(푉) and |푉|푅,푇 is available ̅̅̅̅̅̅̅̅̅ • 퐷푅(푉푇) is the average distance (mi or km) driven each year by a vehicle of type T in region R

Equation (3) allows to modulate the results equation (2) to more granular geographies, where aggregate CO2 emissions and vehicle stock data is not available but where average distance per vehicle-year data is. For example, if C = France, R(C) = European Union and T = Motorcycles, equation (3) reads that the average emission factor of a motorcycle in France was calculated by multiplying the average emission factor of a motorcycle in the European Union with the ratio of the average miles driven by a motorcycle in France to the average miles driven by a motorcycle in the European Union (i.e. if the average miles driven per motorcycle in France is 80% of the average miles driven per motorcycle in the European Union, then emission factor of motorcycles in France is equal to the emission factor of motorcycles in the European Union times 80%).

Equation (3) is also used for all state/province-level emission factors, with C a state or province and R(C) the country of the state/province, e.g. C = California and R(C) = United States.

For countries where neither aggregate CO2 emissions, vehicle stock nor distance/vehicle-year data is available, the PCAF database uses the emission factor of the encompassing region.

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2.2.2. Countries matrix The following table lists the data sources used for each term of equations (2) and (3), for every country in the PCAF database. Please refer to §3 for more details on each source. Table 2. Breakdown of equations (2) and (3) by source for all countries Method Countries 퐷̅̅̅̅ ∑ 퐸; |푉| ; 퐷̅̅̅̅̅̅̅ accuracy 퐶 푅(퐶) 푅(퐶) 푅(퐶) 퐶 = 푅(퐶) for country, United States carinsurance.com for states Very High 퐶 = 푅(퐶) for country Canada Country-level ICCT data CAN OEE for provinces Australia, Brazil, China, India, Japan, Mexico, South Korea, Russia High 퐶 = 푅(퐶) Regional ICCT data: Other Iceland, Israel, New Zealand, Norway, Switzerland, Turkey Europe, Middle-East and Other Asia-Pacific Austria, Belgium, Croatia, Czech Republic, Estonia, Medium Country-level ITF data Finland, France, Germany, Hungary, Ireland, Luxembourg, Netherlands, Slovenia, Sweden, ICCT Regional Data: United Kingdom Bulgaria, Cyprus, Denmark, Greece, Italy, Latvia, European Union Lithuania, Malta, Poland, Portugal, Romania, Slovakia, Spain Algeria, Angola, Benin, Botswana, Burkina Faso, Burundi, Cape Verde, Cameroon, Central African Republic, Chad, Comoros, Congo, Republic of the, Congo, Democratic Republic of the, Cote d'Ivoire, Djibouti, Equatorial Guinea, Eritrea, Eswatini, Ethiopia, Gabon, Gambia, Ghana, Guinea, Guinea- Regional ICCT data: Bissau, Kenya, Lesotho, Liberia, Libya, Madagascar, Malawi, Mali, Mauritania, Mauritius, Morocco, Africa Mozambique, Namibia, Niger, Nigeria, Rwanda, Sao Tome and Principe, Senegal, Seychelles, Sierra Leone, Somalia, South Africa, South Sudan, Sudan, Tanzania, Togo, Tunisia, Uganda, Zambia, Zimbabwe Afghanistan, Bangladesh, Bhutan, Brunei, Cambodia, Fiji, Indonesia, Kazakhstan, Kiribati, North Korea, Kyrgyzstan, Laos, Malaysia, Maldives, Marshall Islands, Micronesia, Mongolia, Myanmar, Region ICCT data: Nauru, Nepal, Pakistan, Palau, Papua New Guinea, Low No country-level data Philippines, Samoa, Singapore, Solomon Islands, Sri Other Asia-Pacific Lanka, Tajikistan, Thailand, Timor-Leste, Tonga, Turkmenistan, Tuvalu, Uzbekistan, Vanuatu, Vietnam Albania, Andorra, Armenia, Azerbaijan, Belarus, Bosnia and Herzegovina, Georgia, Liechtenstein, Regional ICCT data: Moldova, Monaco, Montenegro, North Other Europe Macedonia, San Marino, Serbia, Ukraine Antigua and Barbuda, Argentina, Bahamas, Barbados, Belize, Bolivia, Chile, Colombia, Costa Rica, Cuba, Dominica, Dominican Republic, Ecuador, El Salvador, Grenada, Guatemala, Regional ICCT data: Guyana, Haiti, Honduras, Jamaica, Nicaragua, Panama, Paraguay, Peru, Saint Kitts and Nevis, Other Latin America Saint Lucia, Saint Vincent and the Grenadines, Suriname, Trinidad and Tobago, Uruguay, Venezuela Bahrain, Egypt, Iran, Iraq, Jordan, Kuwait, Lebanon, Regional ICCT data: Oman, Qatar, Saudi Arabia, Syria, United Arab Emirates, Yemen Middle-East

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3. Sources and assumptions

3.1. Make-and-model data

3.1.1. U.S. EPA’s FTP The United States Environmental Protection Agency’s Federal Test Procedure are a series of drive cycle tests to measure the tailpipe emissions and fuel efficiency of passenger cars. Because these tests are used to verify that cars sold in the U.S. meet EPA regulatory standards, their results reflect the road performance of passenger cars in the U.S. The results for more than 4,000 make-and-models are publicly available on fueleconomy.gov, downloadable in .csv format. The names of the FTP data points used and their equivalent terms in equation (1) are as follows: • make → M • model → m • co2TailpipeGpm → 퐸푅퐹푇푃 • combE → 퐸퐸퐹푇푃 • combinedUF → 푈퐹퐹푇푃

3.1.2. EU EEA’s WLTP The World harmonized Light-duty vehicles Test Procedure is a global, harmonized standard of drive cycle tests to determine the tailpipe emissions and fuel efficiency of passenger cars. It was developed by the United Nations Economic Commission for Europe (UNECE) to replace the old New European Driving Cycle (NEDC) as the European vehicle homologation procedure. The NEDC was indeed shown to be flawed, enabling manufacturers to meet EU environmental standards during lab tests but not on the road (Dieselgate). The WLTP was conceived to rectify this. The WLTP is fairly recent: its final version was published in 2015. Hence, even though it will in time become a truly international standard, it is for now only used in the European Union, and its results only reflect the performance of cars sold within the EU. These results are published by the European Environment Agency on their website, in .csv format. Out of the 13,000+ make-and-models that had been tested under the NEDC, 46% have been tested using the WLTP at the time of this methodology document. Across these 6000+ models, the WLTP results are consistently around 23% above the NEDC results (the variance of this percentage is small). For the PCAF database, we have hence used WLTP results for all the models that had them, and multiplied by 1.23 the NEDC results for the models that did not. The names of the WLTP data points used and their equivalent terms in equation (1) are as follows: • Mk → M • Cn → m • Enedc (g/km) and Ewltp (g/km) → 퐸푅푊퐿푇푃 • Z (Wh/km) → 퐸퐸푊퐿푇푃

NOTE: the EEA does not use the utility factor convention used by the EPA for hybrid vehicles. The EEA directly integrates UF into ER and EE, so that equation (1) with 푅 ∈ 퐸푈 and 푇푃(퐸푈) = 푊퐿푇푃 becomes: ̅̅̅̅̅̅̅̅̅̅̅̅̅̅ ̅̅̅̅̅̅̅̅̅ ̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅ 퐸퐹푅(푉푀,푚) = [퐸퐸푊퐿푇푃(푉푀,푚) × 퐸퐹푅(푒) + 퐸푅푊퐿푇푃(푉푀,푚) ] × 퐷푅(푉푇(푀,푚))

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3.1.3. Other regions Beside the FTP, NEDC and WLTP, there are a couple of other test cycles in the world (e.g. in Japan, Australia), but no data is as extensive as the results published by the EPA and the EEA. However, the ICCT has been tracking historical and projected vehicle CO2 emission levels for all the countries included in the ICCT Transportation roadmap (see §3.2.1) and the results, shown in Figure 4, indicate that the EU emission standards stand alone ahead of the rest of the world, which is more aligned around U.S. cars performance. For this reason, PCAF uses results from the U.S. EPA’s FTP for all the other countries in the world that are not in the European Union.

Figure 4. Passenger car CO2 emissions and fuel consumption values, normalized to NEDC

3.2. National and regional data

3.2.1. ICCT Transportation Roadmap The International Council on Clean Transportation (ICCT)’s Transportation roadmap has been a global reference for environmental performance data on all major transportation modes, fuel types and vehicle technologies since 2012. Over the past decade, the ICCT has extended its Roadmap model to cover eleven of the largest vehicle markets (Australia, European Union, Brazil, Canada, China, India, Japan, Mexico, South Korea, Russia and the United States) and five aggregate regions (Africa, Other Asia-Pacific, Other Europe, Other Latin America and the Middle-East). The most recent results of the model (2017) are downloadable in .xlsx format on the ICCT website. The PCAF database currently uses the model’s Baseline Scenario results for year 2020. The data points used and their equivalent in equations (1) and (2) are as follow: • Roadmap_Region → R • Mode → 푇 ̅̅̅̅̅̅̅̅̅ • VKT_billion divided by Stock_million → 퐷푅(푉푇) • TTW_CO2_Mt → ∑푅,푇 퐸(푉) • Stock_million → |푉|푅,푇

13 Motor Vehicle Loans Emission Factors – Database Methodology

The PCAF database currently covers the following transportation modes from the ICCT Roadmap: • LDV → Passenger car • Bus → Bus • 2W_3W → Motorcycle • LHDT → Light Commercial Truck (Light Heavy-Duty Truck) • MHDT_HHDT → Medium/Heavy Commercial Truck (Medium & Heavy Heavy-Duty Trucks)

3.2.2. ITF-OECD Transportation performance indicators The OECD iLibrary maintains a database of Transport Statistics collected by the International Transport Forum on the transport of freight (maritime, air and surface) and passengers (car, rail and air) in its member states. In the ITF database, PCAF uses the Transport performance indicators, which include road traffic per road motor vehicle (in thousand km per vehicle-year) from 2000 to 2015 for most OECD member countries. This data enables to add an additional level of granularity to some of the ICCT Roadmap aggregate regions, mainly the European Union and “Other Europe”. To calibrate the ITF data with the ICCT data, the United States were used as a point of reference. For all the other countries C in the ITF database (excluding Bulgaria, Denmark, Greece, Italy, Latvia, Lithuania and Slovakia for unreliable data), the PCAF average distance driven by vehicle-year was calculated with the following equation: 퐷̅̅̅̅̅̅̅̅(̅푉̅̅̅̅) 퐷̅̅̅̅̅̅̅̅̅̅(̅푉̅̅̅̅) = 퐷̅̅̅̅̅̅̅̅̅̅̅̅(̅푉̅̅̅̅) × 퐶,퐼푇퐹 푇 퐶,푃퐶퐴퐹 푇 푈푆퐴,퐼퐶퐶푇 푇 ̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅ 퐷푈푆퐴,퐼푇퐹(푉푇)

3.2.3. Electricity emission rates Electricity emission rates (in gCO2/kWh) for most countries in the PCAF database are provided by the International Energy Agency (IEA). They are CO2-only emission rates (not including CH4, NO2 nor trade adjustments). For the countries of the PCAF database that were not in the IEA database, a regional IEA emission rate was used (e.g. France for Monaco, Switzerland for Liechtenstein, Non- OECD Asia for Nepal, etc.).

3.2.4. State- and province-level data (USA & CAN) PCAF currently provides state/province-level data for the United States and Canada only: United States • American state-level electricity grid emission rates are retrieved from the U.S. EPA’s eGRID database, using NERC regional grids’ emission rates. • American state-level distance per vehicle-year averages are retrieved from carinsurance.com Canada • Canadian province-level electricity grid emission rates are retrieved from the Residual Mix Emission Rates published by Green-e Energy. • Canadian province-level distance per vehicle-year averages are retrieved from the Canadian Office of Energy Efficiency.