ASG’s environmental life-cycle assessment platform is a revolutionary tool for assessing the environmental performance of automobiles available to consumers in 2019. Prior to ASG's annual study, consumers relied solely on EPA estimated Miles Per Gallon (MPG) ratings and generic EPA reported tailpipe emission scores to assess automotive environmental performance. But MPG – and the directly related tailpipe emissions scores – fail to account for the many life-cycle environmental impacts a vehicle incurs throughout its lifetime.

Other important environmental factors include raw material extraction and production impacts, vehicle manufacturing and assembly impacts, vehicle distribution impacts, fuel source production and distribution impacts, in addition to tailpipe emissions.

By developing a life-cycle model for each vehicle, and reporting vehicle performance in Gross Energy Requirement (GER) and greenhouse gas (GHG) emissions, we provide a platform for comparative analysis across all available technologies in practical use. The results of this detailed analysis are not broad statements about technologies, but rather conclusive statements about individual vehicles that implement different technologies. These results show that not all vehicles implement technologies as effectively, and therefore the trade-offs between conventional vehicles and alternative vehicles must be viewed on a vehicle-by-vehicle basis.



Environmental performance indicators were carefully identified to assess environmental impacts over each vehicle’s life-cycle. ASG approached this task with the development of two distinct models: 

  1. Vehicle Model: assesses total energy and emissions quantitatively for each vehicle, including raw material extraction and material production, vehicle component manufacturing and assembly, and vehicle shipping distribution; 
  1. Fuel Model: assesses total energy and emissions quantitatively for fuel production, distribution and use (combustion or otherwise).



When assessing environmental performance in quantitative models, GER was used as the main indicator for environmental impact, reported in mmbtu[1] of energy throughput. GER is an indicator of energy resource depletion, and accounts for an estimated 90% of the materials depletion aspect (IVF, 2007). GER as reported in this study accounts for not only the direct energy used by the automobile over its life-cycle, but also the indirect energy required to the second and third level. For example, in the Fuel Model, this includes the production of primary energy required to extract and process fuel for electricity generation and distribution (with transmission loss) that would then be directly used to power an electric vehicle.

It is also noted that energy use reported in GER is a more important environmental impact indicator than that of eutrophication and other urban emissions, such as volatile organic compounds (VOCs) and particulate matter. Energy use correlates with acidification and GHG emissions, which are estimated at 100 times more environmentally important than VOCs and eutrophying substances (VHK, 2011). GER is the most important environmental impact indicator (VHK, 2011) when assessing life-cycle impacts, and therefore was used as the primary indicator, along with GHG emissions as the secondary environmental impact indictor.



GHG emissions are reported in Global Warming Potential (GWP), which includes the weighted emissions of GHG emissions in pounds CO2 equivalent, with GWP factors given by the Intergovernmental Panel for Climate Change (IPCC). Global warming potential represents how much a given mass of a chemical contributes to global warming over a given time period compared to the same mass of carbon dioxide (EPA, 2013). The GHG emissions included in the assessment are CO2, N2O and CH4, as is common reporting practice.

The LCA boundary was GER and GHG emissions; we therefore did not assess all effluents and material waste flows.





The first requirement in the Vehicle Model is the determination of vehicle material composition. The Vehicle Model essentially disassembles each vehicle into thousands of individual parts, the sum of which comprise the estimated total bill of materials. It then categorizes parts into distinct groupings to allow for the consolidation of parts that are comprised of the same naturally occurring resources. 

The Vehicle Model assesses GER for each vehicle as a combination of parts comprised of steel, stainless steel, cast iron, wrought aluminum, cast aluminum, copper, brass, magnesium, glass, plastic, rubber and carbon fiber-reinforced plastic. Each vehicle also has a lead-acid battery, the size and weight of which are dependent on vehicle size and type, and analyzed accordingly.



The Vehicle Model assesses GER for lead-acid batteries as a combination of parts comprised of polypropylene, lead, sulfuric acid and fiberglass.

Some hybrid vehicles have nickel–metal hydride (Ni-MH) batteries, which are a combination of parts comprised of iron, steel, aluminum, copper, magnesium, cobalt, nickel, rare earth metals, plastic and rubber, all of which are assessed in GER in the Vehicle Model. Further, some hybrids, PHEVs and EVs contain lithium-ion or lithium-polymer batteries. The Vehicle Model assess GER for these battery types as a combination of parts comprised of lithium oxide, nickel, cobalt, manganese, graphite/carbon, binders, copper, wrought aluminum, cast aluminum, electrolytes, polypropylene, polyethylene, steel, thermal insulation and electronic parts.



With a categorized bill of materials, the Vehicle Model then calculates the GER associated with primary raw material extraction and processing in mmbtu per pound of material product, including secondary material inputs from recycling and reprocessing.



The secondary material inputs are critical in this phase of the model as it expresses the energy saving potential of using materials with high recyclable utility to reduce demand on the more energy intensive primary resource extraction and processing phase. For example, recycled aluminum requires 95% less total energy when compared to virgin aluminum production alone (CU, 2013). Recycled steel requires 33% less total energy to produce when compared to the production of steel from virgin materials alone (Johnson et al., 2008). In the copper industry, raw material mining uses about 20 percent of the total energy requirement, milling around 40 percent, and smelting, converting, and refining the remaining 40 percent (U.S. Congress, Office of Technology Assessment, 1988). Therefore, using secondary recycled materials in vehicle production reduces the need for raw material mining and in turn radically decreases GER. 

In model year 2019, with primary vehicle structures composed of steel, high-strength steel, aluminum and even reinforced carbon fiber, it is critically important to model the appropriate allocation of primary to secondary material inputs and directly related impacts. To the same point, manufacturers have also experimented with different materials in body panels to lessen vehicle weight, particularly in alternatively powered vehicles that incur an often significant vehicle weight disadvantage from large batteries, power converters and electric motors. These material inputs are important considerations in the Vehicle Model.



With a categorized bill of materials, and the GER and GHG emissions calculated for primary and secondary material inputs, the model then begins to reconstruct components of the vehicle in the factory gate. The vehicle components described by the Argonne National Laboratory were incorporated into the Vehicle Model to assess energy intensity of component manufacturing followed by vehicle assembly. Based on the extensive work in this area already conducted by Argonne, including but not limited to research on assembly plant processes and energy use through body welding, assembly and painting (life-cycle of paint included), our model adopted the Argonne framework in this space, but extracted elements of this work so as to make applicable to specific vehicles, rather than generic vehicle models.



With the vehicle now reassembled, the Vehicle Model reincorporates the fluids necessary for vehicle operation, including engine oil, transmission fluid, brake fluid, steering fluid, coolants, windshield washer fluid and fuel. The additional fluid weight adds to the total vehicle weight as it is prepared for transport. The vehicle weight is then used as a critical variable in calculating transport energy requirements.



The total energy required for vehicle transport from assembly plants outside the continental United States to U.S. end-buyers often requires a combination of rail, shipping and truck transport. We therefore developed a Vehicle Transport Model that assessed the GER and GHG emissions, calculated in pounds of emissions per pound-mile traveled, for the international transport of each vehicle assembled outside the continental United States.



We identified 19 countries in which model year 2019 vehicles (destined for North American marketplace) were assembled. Each vehicle’s point of departure originated at its assembly location, with a combination of various transport modes to deliver vehicles to market. Due to the complex distribution system within the United States and the unknown final sales point, the Vehicle Transport Model boundary was the delivery to the nearest U.S. port of entry. This required a Ro Ro Freight Pound-mile Model, a Truck Freight Pound-mile Model and a Rail Freight Pound-mile Model that was applied to each individual vehicle from each point of departure to the North American point of entry.



For those vehicles assembled in the U.S., the destination marketplace, transport delivery impacts were not considered as the point of departure originated within U.S. borders.



The 2019 Vehicle Model does not account for the replacement of batteries throughout the expected vehicle life-term. In past years, the Vehicle Model accounted for two replacement lead acid batteries matching the specs of the original, for conventional vehicles. For hybrids, PHEVs and EVs, the Vehicle Model accounted for two replacement lead acid batteries and one replacement advanced battery module (lithium-ion, Ni-MH, lithium-polymer), matching the specs of the original.

We altered our model in 2019 to hold a consistent approach on replacement parts and components. Since the model did not account for replacement fluids, tires and failing parts over the life of the vehicle, we removed all replacements from the model and adjusted the Average Vehicle Miles Traveled (VMT) assumptions to minimize the influence on conventional and alternatively powered vehicle models. See VMT section below.



In past Vehicle Models, we incorporated the end-of-life energy requirements for dismantling, pre-processing, end-processing (including refining and disposal), and material recovery for re-use of each vehicle. The end-of-life phase provides opportunities to remove environmental burdens through efficient recovery and recycling of critical metals, precious metals, rare earth metals and plastics that can offset impacts associated with primary production, as previously discussed. However, in our 2019 Vehicle Model, we removed end-of-life assessments to better align with our VMT assumptions, described in the Average Vehicle Miles Traveled section below.





The Fuel Model focuses on the use phase of the vehicle, including vehicle operation and fuel input cycles. We first developed a Vehicle Miles Traveled (VMT) Model to forecast the average miles each vehicle will likely travel over the course of its lifetime. The existing research in this area presents confounding results however. We therefore scrutinized the existing body of research for errors and omissions and determined the average VMT is best represented when based on combined data from the Oak Ridge National Laboratory (Davis et al., 2009) and data presented by Bandivadekar (2008). This too was the VMT assumption made by Cheah (2010) at MIT’s Sloan Auto Laboratory.

We concluded a new passenger vehicle will be driven an average 227,200 miles over its lifetime. It has been noted that VMT has risen steadily over past decades: 1.7% annual rise from 1971 to 2005. This rise has been reduced to 0.5% per year forecasted through 2020, 0.25% from 2020-2030 and finally to 0.1% from 2030 and beyond (Bandivadekar, 2008). The increase in VMT has been driven upwards by investments and growth in highway infrastructure, low gasoline prices, income growth, and demographic trends that include greater labor force participation (Cheah, 2010).

We also assume new passenger vehicles are driven an average of 13,476 miles per year (FHWA, 2016) over the first 6.5 years. With new car buyers continuing a trend of driving their vehicles longer, the average length of ownership is now at 79 months (IHS Markit). By combing the FHWA average annual VMT, and the IHS Markit average length of vehicle ownership, we assumed the new car buyer would travel an average 87,594 VMT in the first 6.5 years of vehicle ownership. Focusing our assessment on the first 6.5 years of vehicle ownership fully aligns with our Economic Performance assumptions, and provides an improved comparison between conventional and alternatively powered vehicles.  These revised assumptions provide clarity for a more direct and consistent vehicle comparison of the new car marketplace. It also reduces the margin of error in forecasted assumptions on electricity grid improvements (CO2 per kWh produced) and fuel costs.



The Fuel Model then combines the VMT modeled data with each vehicle’s fuel economy specifications to calculate fuel requirements in volume. Fuel volume is then assessed in terms of GER for extraction, processing and delivery to the fuel pump station. This cycle is often called well-to-pump. The well-to-pump assessment is critical to the comparison of vehicles that operate from different fuel types and that display different fuel to energy conversion efficiencies – such as comparing a gasoline powered vehicle, with diesel, hybrid, PHEVs and EVs.



While it can be said that EVs do not produce tailpipe emissions, this is only half of the story, as the production and distribution of the electricity used to power that EV indeed produces GHG emissions upstream from well-to-plug. These emissions are accounted for in the Fuel Model for both EVs and PHEVs.

The energy mix in the U.S. is a critical consideration for EVs and PHEVs – the cleaner the energy grid, the greater the emission reduction potential of these vehicle technologies. We developed a Grid Improvement Model to forecast the year over year improvement in the U.S. electricity grid  – lessening pounds per CO2 equivalent emissions per kWh produced (considering a 8.33% transmission loss). This model was developed from historical trends showing grid improvements as reported by U.S. EPA in the recent decade. Our model assumes an incremental grid improvement that is consistent with historical evidence; however there is the potential of disruptive scalable technologies entering the market abruptly and that which have the potential of improving the grid on a rate never seen before. Such technologies would further reduce EV and PHEV emissions. 



The Fuel Model then combines the fuel requirements in volume as calculated in the well-to-pump phase to assess the emissions produced during operation. This is often called the pump-to-wheels phase. Combustion emissions of conventionally and alternatively powered engine configurations differ by fuel source and fuel-to-power conversion efficiencies. For example, diesel fuel has a higher energy content per gallon than gasoline, but due to the higher density of diesel, it also releases more GHG emissions per gallon combusted. This disadvantage is often overcome in diesel configurations by greatly improved fuel economy, inherent to lean burning engines, but fuel economy improvements must be sufficient to offset the combustion disadvantage in order to reduce pump-to-wheel emissions comparatively.

The PHEV is by far the most complicated model to develop given the dual mix of fuel sources and mileage allocation. This model adopted the Gamma Distribution Methodology (Lin et al, 2011, 2012) to estimate the daily VMT and its distribution to gasoline and electric fuel sources. The gasoline and electricity allocation for PHEVs are determined by dynamometer-based performances, powertrain control algorithms, charging patterns and driving patterns that were developed by researchers at the National Transportation Research Center at Oak Ridge National Laboratory and the Vehicle Technologies Program at the U. S. Department of Energy, Office of Energy Efficiency and Renewable Energy (Lin et al, 2012). We applied the average U.S. electricity mix to electric power shares of VMT, but also incorporated the Grid Improvement Model forecasts to account for improvements in energy mix across future years.



The complete Fuel Model is a well-to-wheels assessment for each vehicle, allowing different fuel types and different fuel-to-power conversion efficiencies to be compared in GER and GHG emissions. This is the first study to complete comprehensive well-to-wheels assessment across an entire fleet of vehicles that are currently available to consumers for purchase.


[1] mmbtu is one million btu (British thermal units). One btu is equal the amount of energy required to cool or heat one pound of water by one degree Fahrenheit.




The Automotive Performance Index (API) applies statistical methods to demonstrate each vehicle rating in relative comparison. For example, the vehicle that performs highest in environment performance in a given class obtains a rating score of 100. Each vehicle in its class is then compared relative to the top-performing vehicle with a rating score reflecting the statistical difference in performance outcomes. A score of 91 translates to a 9% environmental performance deficit as compared to the top-performing vehicle.  

Due to the API’s relative vehicle rating method, vehicle class divisions were identified as a critical input - these class divisions are detailed here.

While the Automotive Performance Index is indeed an exhaustive list of vehicles to trim level detail, with each vehicle assessment reporting over 200 unique data outputs (the culmination of thousands of data inputs), ASG has taken additional measures to ease the burden of sorting through all data points and all vehicle assessments. We have developed key performance categories and sorted all vehicles in each class according to Environmental Performance, Social Performance, Economic Performance and All-Around Performance. The vehicle in each class with the best score in each unique category is named the performance award winner (i.e. Best Environmental Performance Award winner). The vehicle in each class that scores highest combined scores in Environmental Performance, Social Performance and Economic Performance is named the ASG Best All-Around Performance Award winner.

One step further, we also name the Best 5 All-Around Performance Award winners in each class to provide consumers with a concise product comparison guide.