The energy, material and carbon handprint of lithium-ion batteries in electric vehicles

The energy, material and carbon handprint of lithium-ion batteries in electric vehicles | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The energy, material and carbon handprint of lithium-ion batteries in electric vehicles Pablo Busch, Yunzhu Chen, Alissa Kendall This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8436282/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 10 You are reading this latest preprint version Abstract Electrifying the transport sector will require manufacturing of lithium-ion batteries and extensive mining of their embedded critical minerals, like lithium. Research has quantified the future demand for lithium-ion batteries, their constituent materials and their environmental impacts, but typically without contextualizing these impacts within the benefits of fleet-level decarbonization. We conduct a life cycle assessment of the United States projected light-duty electric vehicle fleet (2025–2050) and compare it to a counter-factual scenario where all electric vehicles are instead internal combustion engine vehicles to determine the environmental benefits enabled by lithium-ion batteries in electric vehicles. Results show that electric vehicles will reduce primary energy consumption by 20%, material extraction (including fossil fuels) by 34% and carbon dioxide equivalent (CO 2 e) emissions by 61% compared to an internal combustion engine-only future. This translates into 300–600 kg CO 2 e avoided per kilowatt-hour of lithium-ion battery, or 5–12 tons CO 2 e avoided per kg of lithium extracted. Under conditions of high battery recycling rates, avoided emissions can increase to 20 tons CO 2 e per kg of lithium. However, electric vehicle deployment increases metal extraction by 117% and critical minerals extraction by 179%. Actions to reduce the metal intensity of EVs are needed such as increasing LIB durability, improving EV energy efficiency, and enhancing battery recycling and metal recovery rates to avoid new mining and multiply the climate benefits of battery mineral extraction. electric vehicles lithium-ion batteries life cycle assessment critical minerals greenhouse gas emissions Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. INTRODUCTION The electrification of road transportation is crucial for reducing greenhouse gas (GHG) emissions from the transport sector. Battery electric vehicles (EVs) powered by lithium-ion batteries (LIBs), emit lower GHG and tailpipe pollution per kilometer driven than internal combustion engine vehicle (ICEVs), but have higher impacts in their manufacturing supply chains (Hawkins et al., 2012 , 2013 ; Lombardi et al., 2017 ; Verma et al., 2022 ). Adoption of EVs has grown significantly; in the United States (US) sales exceeded 1.5 million units in 2024, reaching 10% of all new light-duty vehicle sales, with battery electric vehicles (BEVs) making up around 80% of those sales and plug-in hybrid electric vehicles (PHEVs) making up the balance (ICCT, 2025 ). EV sales are expected to keep growing, increasing the demand for critical minerals contained in the LIBs, particularly lithium, cobalt, and nickel (IEA, 2024 ), which raises concerns about the life cycle environmental impacts, both global and local, of vehicle electrification (Bouter & Guichet, 2022 ; Dolganova et al., 2020 ). Previous life cycle assessment (LCA) studies on road electrification, and especially those studies focusing on LIBs in EVs, have primarily focused on quantifying the environmental burdens associated with LIB manufacturing, even when EVs and ICEVs are examined in a comparative context (Hawkins et al., 2013 ; Lombardi et al., 2017 ; Onat et al., 2015 ; Verma et al., 2022 ). At the battery system level, studies have quantified GHG emissions across different LIB chemistries (Ambrose & Kendall, 2016 ; Bouter & Guichet, 2022 ; Lai et al., 2022 ) or across various LIB recycling technologies (Dunn et al., 2012 ). At the vehicle level, multiple studies have compared EVs and ICEVs, finding that EVs generate lower life cycle GHG emissions per kilometer traveled under nearly all conditions considered (Lombardiet al., 2017 ; Woody et al., 2022 a). The carbon intensity of the electricity grid is a key determinant of the climate benefit of EVs, as in regions with high-emission grids the advantage of EVs may be reduced or even outweighed, particularly when compared to high-efficiency gasoline hybrid electric vehicles (HEVs) (Singh et al., 2024 ). Other factors, including vehicle size, ambient temperature, battery degradation, charging schedule, and driving patterns, also influence GHG vehicle emissions (Ambrose et al., 2020 ; Archsmith et al., 2015 ; Onat et al., 2015 ; Singh et al., 2024 ; Woody et al., 2022 , 2023 ). Product-level LCA studies focus on the environmental burdens of individual and specific LIBs and EVs, providing valuable insights into material- and technology-level comparisons, but offering limited insight on the system-wide implications of a large-scale transition to electric mobility and the system-level benefits that LIB production and mineral extraction can enable. Past fleet-level assessments omit the role of critical minerals in enabling the climate benefits of EVs. Although several modeling studies have projected future demand and supply for critical materials, such as lithium under various electrification scenarios (Busch et al., 2025 ; Woodley et al., 2024 ), few studies have quantified how the extraction of these minerals translates into environmental benefits across the fleet, or analyze how battery downsizing or circular economy actions may increase the environmental benefits per kg of mineral extracted. There is a need to evaluate the broader sustainability implications of large-scale EV deployment, and how effectively LIBs and mineral extraction are contributing to climate mitigation through electrification. The concept of a “handprint” was introduced in 2007 to describe the beneficial environmental outcomes of an action (CEE, 2007 ). Norris & Phansey ( 2015 ) later defined a handprint as a footprint-consistent estimate of the positive changes resulting from an intervention. This concept has since been incorporated into LCA studies and is best evaluated using a system-level scope. In recent literature, handprints are typically represented as avoided impacts relative to a reference scenario, emphasizing their role in quantifying positive climate outcomes (Alvarenga et al., 2020 ). In this study, we apply the handprint concept within a comparative LCA framework to evaluate the climate benefits associated with LIB manufacturing and lithium extraction for EV deployment. Our scope is the projected EV fleet in the US from 2025 to 2050, and the counter-factual comparison scenario of an all-ICEV future instead. The novelty of this work includes a system-level scope that includes the whole US light-duty fleet, using updated data sources for EV sales projections, battery size and chemistry, electricity grid forecasts and evaluation of the life cycle energy, material and carbon intensity of both futures. The fleet level model allows us to determine the climate benefits of LIBs and lithium, and how different actions, such as lifetime extension and recycling, can increase their climate benefits over time. 2. METHODS We conducted a comparative LCA for the whole fleet of the US from 2025 to 2050, contrasting a 100% EV adoption scenario with an ICEV-only counterfactual scenario. We omit from both scenarios the sales and stock of baseline ICEV, hybrids and hydrogen-powered vehicles, and thus we are only comparing the effects of projected EV sales and projected ICEV sales under the two modeled scenarios. We refer here to EVs as vehicles powered solely by traction LIBs, as they constitute the majority of future electric vehicle sales (ICCT, 2024 ), thus omitting all powertrains, even PHEVs, that have gasoline engines. The LCA scope includes the stages of vehicle and battery manufacturing, vehicle maintenance; driving; vehicle and battery turnover based on survival curves; carbon intensity from the electricity grid based on energy mix forecasts; and end of life management of batteries and vehicles (Fig. 1 and Table S1 for full data sources details). The main metric of comparison is GHG emissions expressed in units of CO 2 -equivalent calculated using 100-year global warming potential (GWP) factors that exclude land use and biogenic emissions (IPCC, 2023 ). We also quantify primary net energy consumption and material usage (non-renewable elements), as well as applying TRACI impact assessment methods for ozone depletion, photochemical oxidation, air acidification and human health particulate air pollution emissions (EPA, 2021 ). For GWP, we also tested time-adjusted global warming potentials (TAWPs) for a 100-year period, to account for differences in emissions timing between EVs and ICEVs (Kendall, 2012 ). 2.1 Fleet Stock We estimate vehicle stocks using a fleet model based on a forecast of vehicle sales and vehicle and battery survival curves, at geographic resolution of each US state ( s ), as described in Eq. 1 , where t refers to years, s refers to states (location), and age refers to vehicles of a given age within the fleet: $$\:Flee{t}_{t,s}=Flee{t}_{t-1,s}+Sale{s}_{t,s}-\sum\:_{age=0}^{30}\left(Flee{t}_{t,s,age}*Vehicle\:Failure\:fractio{n}_{age}\right)$$ 1 To build the stock model, historical light-duty EV sales (starting from 2016) are taken from EV Volumes (J.D. Power, 2025 ). Light-duty EV sales forecasts for the US are drawn from the International Council on Clean Transportation (ICCT) Roadmap v2.6 “Ambitious” scenario (ICCT, 2024 ). The Ambitious scenario assumes 100% light-duty EV sales by 2035 for the US. EV Volumes and the ICCT Roadmap report sales on a national scale. We disaggregated them to state level following historical EV registration records (DOE, 2024 ) and using a liner interpolation towards population share by state in 2035 (U.S. Census Bureau, 2025 ) (SI Figure S1 ). Sales were disaggregated into vehicle type (cars and light-trucks) by region following the annual energy outlook (AEO) estimates of LDV fleet composition (EIA, 2025b ) (SI Figure S2). We consider that vehicles and batteries can fail independently following the product-component framework (Aguilar Lopez et al., 2022 ). We follow previous lifetime assumptions for EVs and LIBs (Busch et al., 2025 ), both following a logistic distribution with mean 17 years for vehicles and 15 years for LIBs, with a 4-year standard deviation. LIB failure in a working vehicle generates inflows (replacement) and outflows of LIBs (Fig. 1 ). We consider that a vehicle younger than 8 years will get a new battery covered by the warranty, and that a vehicle above 12 years will be scrapped if their battery fails. If available, a vehicle aged 8 to 12 may get a battery in good condition (coming from a vehicle-only failure) or a new LIB. We consider that 25% of batteries in good condition coming from vehicle failures are available to be reused in other vehicles, while the rest of batteries go towards recycling. For the counterfactual of ICEV, the same lifetime distribution is assumed for vehicles. 2.2 Manufacturing Manufacturing impacts are estimated by multiplying the required vehicles and batteries (new and for replacement) with their upstream unitary impacts. We use upstream life cycle inventory (LCI) data primarily sourced from the ecoinvent 3.11 database (SI Table S1 lists the LCI datasets used). For modeling the LCA of vehicle manufacturing we distinguish vehicle types (car and light-trucks) based on their different curb weight (Argonne National Laboratory, 2024 ). For BEVs we include battery production upstream impacts combined with battery capacity estimates as a function of vehicle type and current cathode chemistries in the US (SI Figure S3) (J.D. Power, 2025 ). The data sources for vehicle sales and LIB replacements are explained in detail in the Fleet Stock section. For impacts associated with vehicle and LIB manufacturing we consider an amortization (negative impact due to remaining lifetime) at year 2050, assuming a vehicle and LIB working lifetime of 15 years. For example, for a vehicle that was produced in 2040, we subtract one third of its manufacturing impact in 2050 to account that the vehicle still has remaining working lifetime. 2.3 Vehicle Driving We estimate total driving energy consumption, in electricity or gasoline, by multiplying the vehicle fleet by age at each year (SI Figure S4) by their annual vehicle miles traveled (VMT) (SI Figure S5) and vehicle energy consumption per mile (SI Figure S6), both parameters dependent on vehicle age (see Eq. 2 ). $$\:Energy\:con{s}_{t,s}\:\left[kWh\:or\:liters\:gas\right]=\sum\:_{age=0}^{30}\left(Flee{t}_{t,s,age}*VM{T}_{age}*mpg{e}_{t,age}\right)$$ 2 Energy consumption data is from Environmental Protection Agency (EPA 2-cycles), so we adjust them to reflect real world driving conditions and differences in weather across the US. We use temperature adjustment factors, accounting for both warm and cold conditions, at county level, provided by (Woody et al., 2022 ), differentiated for EVs and ICEVs. We consider an efficiency degradation by age of the vehicle 1% per year for ICEVs and 1/3% per year for EVs (Olguín et al., 2025 ). EV charging losses are already embedded in the energy consumption factors (EIA, 2025b ) The impact per kWh of electricity consumed in an EV depends on the electricity generation method and their upstream impacts. We use the electricity generation forecast by EIA for all the contiguous US states, and EPA eGRID for Hawaii and Alaska to estimate the future grid mix per energy market module region (SI Figure S7). Electricity generation impact per energy source was obtained from ecoinvent 3.11, for different US regions (SI Table S2). Electricity balancing regions in the US do not necessarily conform to county boundaries. We combine spatial polygon data layers of electricity balancing regions and counties by assigning each county a specific region based on the largest area overlap, and then we estimate the state average grid mix using county population as weights. We include 5% transmission and distribution losses (EIA, 2023 ). Overall, the US grid will reduce its carbon intensity by 2050 (SI Figure S8) due to higher renewable energy adoption. Gasoline combustion impact was estimated using total gasoline consumption with associated combustion and gasoline production emissions (e.g., oil extraction and refining), obtained from ecoinvent 3.11. 2.4 Recycling We consider that all vehicles are recycled at end-of-life, with the associated LCI impacts of the recycling process. We use the following assumptions for material recovery: steel (96% recovery), aluminum (91%), lead (99%), nickel (80%), magnesium (70%) and copper (50%) (Argonne National Laboratory, 2024 ; Glöser et al., 2013 ; Kumar et al., 2023 ). We consider battery recycling at different recycling collection levels, assuming recovery rates of 90% for nickel, cobalt and lead; 80% for lithium; and 95% for copper, rare earth elements (REE), manganese and aluminum (European Parliament, 2023 ). For material usage results we assume that the recovery of these metals avoids new mineral extraction. From a life cycle accounting perspective, this means we credit recycling with avoided lithium extraction impacts assuming an equal mix of lithium production from hard-rock and concentrated brines. 2.5 Carbon Handprint We estimated the carbon handprint of LIBs and lithium as the avoided GHG emissions, in units of CO 2 e, due to the adoption of EVs instead of ICEV. The handprint for LIBs is estimated per kWh of battery storage manufactured: $$\:LIB\:Handprint=\frac{{\sum\:}_{t}(GH{G}_{EV\:Fleet}-GH{G}_{ICEV\:Fleet}\left)\right[tons\:C{O}_{2}e]}{{\sum\:}_{t}LIB\:Requirement\:\left[kWh\right]\:}$$ 3 In a similar way, the handprint of lithium can be estimated per kg of lithium extracted: $$\:Lithium\:Handprint=\frac{{\sum\:}_{t}(GH{G}_{EV\:Fleet}-GH{G}_{ICEV\:Fleet}\left)\right[tons\:C{O}_{2}e]}{{\sum\:}_{t}Lithium\:Extraction\:\left[kg\right]\:}$$ 4 We tested different parameters for the carbon handprint of LIBs and lithium. For the electricity grid, besides the “2025 reference” scenario, we consider the EIA extreme scenarios in fossil fuel adoption of “low oil & gas supply” and “high oil & gas supply”; along with using the forecasted mix by the Cambium model (EIA, 2025b ; NREL, 2025 ). We tested a short battery lifetime scenario with 10 years average lifetime, and a long battery lifetime scenario with 20 years. We use the 95th quantile of battery size to construct scenarios on battery capacity (J.D. Power, 2025 ), using 68 kWh and 83 kWh for cars and light trucks in a low-capacity scenario, and 100 kWh for cars and light trucks in the high-capacity scenario. For energy consumption in ICEVs, we consider a scenario with a 15% linear improvement in miles per gallon (mpg) towards 2040 (SI Figure S6). Finally, we consider different LIB recycling collection rates: 0%, 40% and 80%. 3. RESULTS On a life cycle basis, the EV fleet has lower energy consumption, material extraction and GHG emissions than a counterfactual ICEV fleet (Table 1 ). Table 1 Primary energy consumption, material extraction and GHG emissions from the US future EV fleet (2025–2050) and a counter-factual ICEV fleet. Metric EVs ICEVs Primary Energy, TWh 71,155 89,252 Oil 5,952 69,647 Coal 15,914 8,655 Natural Gas 17,403 8,244 Uranium 11,861 1,278 Renewable 19,654 1,416 Other 370 11 Materials, million tons 5,445 8,264 Fossil Fuels 4,521 7,922 Non-Metal 203 9 Metal 721 332 Critical Minerals 230 82 GHG, million tons CO 2 e 9,690 24,893 3.1 Primary energy consumption A future all-EV fleet will require 20% less primary energy consumption than a counterfactual ICEV fleet. The higher battery-to-wheel efficiency (85%-90%) from EVs (Ezugwu et al., 2025 ; Gustafsson & Johansson, 2015 ; Weiss et al., 2020 ) more than compensates for the additional energy consumed in LIB manufacturing and energy losses in electricity production, distribution and charging (Table 1 and Fig. 2 ). On a life cycle basis, ICEVs will consume 1.25 kWh per km driven on average and EVs consume 1 kWh per kilometer driven. However, EVs consume 42% less non-renewable energy than ICEVs, because of renewable energy resources on US grids. At fleet level, the reduction translates to major savings in energy consumption: the counterfactual future of an ICEV fleet will require the consumption of 70,000 TWh of oil (equivalent to 41,000 million oil barrels), along with consumption of 8,500 TWh coal (vehicle production mainly) and 8,000 TWh of natural gas (vehicle production and oil refining). The EV adoption scenario will require the consumption of non-renewable primary energy for electricity generation and vehicle (including LIB) manufacturing: 16,000 TWh of coal, 17,000 TWh of natural gas, 6,000 TWh of crude oil and 12,000 TWh of uranium (nuclear energy). The majority (81%) of energy consumption for ICEV occurs at the driving stage, associated with gasoline consumption, while for EVs driving accounts for 55% of total primary energy consumption, with vehicle production consuming 22% and LIB production 23%. The latter shows that further decarbonization of EV supply chains, especially LIB manufacturing, could have a substantive effect on reducing life cycle energy consumption. 3.2 Material consumption ICEVs has a 52% higher material consumption than EV, mostly driven by the fossil fuel extraction requirements (Table 1 and Fig. 3 ). This result challenges the common claim that EVs are more material intensive than ICEVs, and reinforces the claim that the energy transition will have less mining requirements than our current fossil fuel system (Nijnens et al., 2023 ). Moreover, fossil energy materials are not recoverable after use, eliminating the possibility for circularity and future reductions in primary material extraction. The counter-factual future of an ICEV fleet will require the extraction of 1,400 million tons of coal, 6,000 million tons of crude oil and 800 billion m 3 of natural gas, while the EV fleet scenario will require 2,500 million tons of coal (86% more), 500 million tons of oil (92% less), and 1,800 billion m 3 of natural gas (110% more). Given the high energy density of uranium in nuclear energy generation, the EV scenario will only require 76,000 tons of uranium. The EV fleet has 117% higher metal consumption than ICEV, mainly driven by the additional material requirements in LIB manufacturing (Fig. 3 ). The vast majority of additional metal requirement is iron for steel production, which can be recovered at end-of-life at high rates (96%) (Argonne National Laboratory, 2024 ). Our results also omit improvement in LIB manufacturing efficiencies or changing cathode chemistries which could decrease metal intensity per kWh manufactured. In terms of critical minerals, as defined by the USGS (Nassar et al., 2025 ), EVs have a 179% higher (or 126% in the high recycling scenario) requirement than ICEV, mostly from higher copper, silicon and aluminum usage in LIBs. The future EV fleet will require the manufacturing of 34 TWh of LIBs with the associated extraction of 1.4, 15 and 0.9 million tons of lithium, nickel, cobalt, respectively. While the consumption of critical minerals is higher in EVs, they can recover at LIB end-of-life thus reducing the amount of primary material extraction to 0.5, 10 and 0.5 for lithium, nickel and cobalt under a high recycling assumption. If we consider the total service provided by vehicles in kilometers driven, the consumption of metals in EVs is in the order of magnitude of 1 kg per 100 km driven, and for critical minerals is around 1 kg per 315 km driven. EVs require 1 kg of lithium per 50,000 kilometers driven (or per 80,000 km under high recycling), 1 kg of nickel per 5,000 km (7,000 with recycling), and 1kg of cobalt per 80,000 km (135,000 with recycling). 3.3 Carbon emissions As a direct consequence of lower non-renewable primary energy consumption, the carbon footprint of an EV fleet is around 135 gCO 2 e per km, a 61% reduction from the footprint of 346 gCO 2 e from an ICEV fleet (Table 1 and Fig. 4 ). In absolute terms, the future EV fleet for the US will generate 9,500 million tons of CO 2 e, and the ICEV counter-factual fleet generates 25,000 million tons. The stage disaggregation of GHG emissions follows non-renewable primary energy usage (Fig. 2 ), where driving constitutes the majority of emissions for ICEV (85%), while for BEVs it is equally divided into vehicle production (35%), LIB production (31%) and driving (34%). Even with improvements in efficiency for the future ICEV fleet and in the large emissions from US EV manufacturing due to battery size, durability or electricity for EVs, the difference in emissions remains strongly in favor of EVs, mostly due to lower emissions on the driving usage and the barriers to abating combustion emissions from gasoline. Using upstream GHG emission factors for vehicle and LIB manufacturing from the GREET model further increases the difference in the carbon footprint between an EV and a counterfactual ICEV fleet (Argonne National Laboratory, 2024 ), with the EV fleet having 69% fewer GHG emissions, and an average emission of 92 gCO 2 e per km (SI Figure S9). The GREET model assumes fewer GHG emissions in LIB manufacturing, so the majority of emissions come from driving (50%), vehicle manufacturing (30%) and LIB production (20%). Assuming long-run marginal electricity emissions factors provided by the Cambium model (NREL, 2025 ), instead of average grid mix, increases the driving emissions from EVs, but on a life cycle basis they still have 55% fewer emissions than an ICEV fleet (SI Figure S10). The timing of emissions is different for both scenarios, with EVs having higher upfront emissions due to manufacturing requirements that are sustained over time and lower driving emissions as the electricity grid gets cleaner, and ICEVs having increasing driving emissions over time due to driving efficiency degradation. The timing of GHG emissions or removals has a demonstrative effect on their global warming impact, which is not captured in typical carbon accounting methods and global warming potential. Brandão et al. ( 2019 ) offer a review of alternative methods, including those that retain the unit of CO 2 -equivalent for compatibility with traditional LCA methods. Here we use one of those methods, Time Adjusted Warming Potentials (TAWPs), which results in a unit of CO 2 -equivalent emitted today (Kendall, 2012 ). After applying this method, we find that the relative difference between EV and ICEV futures remains essentially unchanged from the case where traditional GWPs are used, meaning that emissions timing is not significant for this assessment (SI Figure S11). For other impact categories, EVs have 30% higher emissions of SO 2 eq (acidification), 65% higher emissions of PM 2.5 eq (human health), 27% higher emissions of O 3 eq (smog formation) and 71% lower emissions of CFC 11 eq (ozone depletion) (SI Figure S12). The higher impact for acidification, human health particulate matter and smog formation is driven mainly by LIB production. The total damage resulting from these emissions will depend on the location of the impact, so more detailed spatial LCA methods are needed to assess the local environmental damage of the EV supply chain. 3.4 Carbon handprint of lithium and LIBs EVs have lower carbon emissions than their ICEV counterparts (Fig. 4 ), meaning that LIB and lithium act as technology enablers to generate a net positive climate effect. We estimate the carbon handprint of LIB production and primary lithium extraction as the total emissions avoided resulting from both fleet comparisons (EV vs ICEV, Fig. 5 ). The handprint already accounts for LIB manufacturing and lithium extraction emissions. Each kWh of LIB manufactured for an EV avoids 450 kg of CO 2 e, moreover each kilogram of primary lithium extracted avoids 11 tons of CO 2 e (assigning all reductions to lithium, and none to other critical minerals in the LIB, such as nickel, cobalt, graphite or copper). In a scenario with shorter lifetimes and bigger LIBs, the handprint is still around 250 kg CO 2 e avoided per kWh of battery and can be as high as 650 kg CO 2 e per avoided kWh in a scenario with long battery life and smaller LIBs. Similarly, the lithium handprint with no recycling can be as low as 5-ton CO 2 e avoided/kg Li and as high as 12-ton CO 2 e avoided per kg of Li, depending on the battery size and durability assumptions. Notably, the forecasted grid electricity has a relative minor effect on the handprint of LIBs and lithium. Lithium can be recovered at end-of-life from LIBs, thus reducing the amount of virgin lithium extraction and increasing the climate benefits of lithium (as it can be used in multiple batteries). The different recycling levels have the biggest effect in the lithium handprint, with a potential benefit over 23 tons of CO 2 e avoided per kg of primary lithium extracted in an 80% recycling scenario (64% net recovery, as we assume 80% of lithium is recovered in the hydrometallurgical process). 4. DISCUSSION A nation-wide light-duty fleet level comparison allows us to analyze the system change in carbon emissions, material usage and energy consumption for the US in the next 25 years (2025–2050). In energy terms, EVs provide two main advantages over ICEVs: higher overall efficiency from well-to-wheel and diversification of primary energy sources. We find that EVs consume 20% less primary energy than ICEV, even after accounting for LIB manufacturing and losses in electricity generation, transmission and distribution. The main reason is due to the high battery-to-wheel efficiency (85%-90%) (Ezugwu et al., 2025 ), while ICEV vehicle tank-to-wheel efficiency is lower (14%-33%) (Albatayneh et al., 2020 ). The energy supply of ICEV is mainly crude oil for gasoline, and this concentration leads to geopolitical risks and import-dependence (Cheng et al., 2025 ). The energy inputs for EVs are much more diversified coming mainly from coal for vehicle production, and natural gas, uranium and a growing share of renewable sources for electricity generation. Further advancements in cleaning the electricity grid will further diversify their energy source and decarbonize EVs in all life cycle stages. EVs have a 34% lower material footprint than ICEVs, mostly driven by the high fossil fuel material extraction needed to sustain the lifetime operation of an ICEV. However, EVs have a 117% higher metal and 179% higher critical minerals footprint, mainly due to higher material usage in the manufacturing of LIBs. We found that EVs consume much more iron than ICEV, a high-grade ore mineral (around 60%) (Tuck et al., 2022 ), and that the emerging LIB manufacturing supply chains could become more efficient over time in reducing material scrappage and increasing recovery. For critical minerals, EVs consume much more copper, aluminum, nickel, cobalt and lithium, generating potential risks and environmental impacts on the expanding mining supply chains. The higher critical mineral intensity of EVs is an important area to improve by demand-side actions such as increasing LIB longevity, improving energy efficiency, expanding the charging infrastructure and exploring new LIB chemistries (Busch et al., 2025 ). Metals, as opposed to fossil fuels, can be recovered and re-introduced to the economy with recycling, thus creating a secondary source of minerals and preventing virgin material extraction. However, the fast adoption of EVs will require massive extraction in the next 15 years until mineral stock is built into the economy and most LIBs start becoming available for recycling (J. Dunnet al., 2021 ). EVs have 61% lower GHG emissions than ICEV, mainly from their lower energy consumption and their higher share of low-carbon energy sources (solar, wind, hydro and nuclear). The difference remains even by accounting for different scenarios and timing of emissions. Moreover, EVs have a direct pathway to keep reducing their carbon footprint. The majority of ICEV emissions come from gasoline combustion, which could only be partially mitigated through efficiency improvements or with biofuel substitutions, which may entail heavy land-use changes impacts. Our results indicate that life cycle carbon emissions of EVs come from three equally proportional stages: vehicle manufacturing, LIB production and driving; and all these stages will benefit from improvements in global electricity grid emissions (as manufacturing occurs outside the US). Reductions in material usage, such as lightweighting or better manufacturing process, will also generate carbon reductions (Wiedenhofer et al., 2025 ). Multiple studies have highlighted lithium extraction and LIB manufacturing environmental impacts, with less attention to their climate benefits in decarbonizing the light-duty transport sector. We found that LIB production and lithium extraction enables major life-cycle carbon reductions in EVs, in the order of 0.3 to 0.6 tons CO 2 e avoided per kWh of LIB and 5 to 23 tons CO 2 e avoided per kg of lithium extracted. The latter assumes full allocation of the benefits to lithium, while other metals are required in LIBs, but it illustrates that mining for critical minerals will provide multiple climate benefits that can be sustained over time, especially with circular economy actions as most battery minerals can be recycled back into cathode grade materials. Mining does generate major environmental impacts (Giljum et al., 2025 ), such as water consumption (Islam et al., 2025 ), land-use change and deforestation (Mervine et al., 2025 ). A priority for the energy transition should be to mitigate mining impacts through demand-side actions to reduce minerals requirements and prevent unnecessary mine openings (Busch et al., 2025 ). The recovery of minerals through recycling offers multiple benefits such as lower energy requirements, less ecosystems disruption and a long-term domestic supply for minerals. While we have demonstrated the lower energy, material and carbon impacts of EVs with respect to ICEV, they do have higher metal consumption and higher impacts on acidification, human health (particulate matter) and smog formation that should be actively managed to reduce damage to humans and ecosystems. Our analysis has multiple limitations that span directions for future research. In the modelling aspect, we consider the same lifetime of EVs and LIB for all states in the US and all LIB chemistries, even as factors like climate may alter the durability of each component. To characterize manufacturing impacts, we rely on static LCI background data with no efficiency improvements, thus resulting in a likely overestimation of impacts for the manufacturing stage. This is especially relevant for EVs, as current LIB manufacturing has a high energy and material usage that has the potential to improve with manufacturing advances and cleaner electricity grids in the major producing countries, like China. Our counterfactual scenario only considers EV and ICEV, omitting hybrid vehicles. We consider only one scenario of number of EV sales, ignoring that vehicle production can be prevented with better access to repair, or reductions in vehicle ownership per capita with public transit or high-occupancy vehicle rides. These are policy options that will reduce the energy, material and carbon impacts of the light-duty transportation sector by providing the same mobility benefits. For electricity emissions we are using the average grid mix, omitting the construction of transmission and distribution infrastructure to meet the growing power demand for charging electric vehicles. For the material usage impact, we are not counting removed ore due to mining, which can generate higher mass removals especially for metals with low ore grades (Wang et al., 2025 ). 5. CONCLUSION We have shown that the future US EV light-duty fleet will reduce the primary energy consumption, material usage and GHG emissions compared to a counterfactual ICEV fleet. The manufacturing of one kWh of LIB can avoid up to 600 kg of CO 2 e emissions, and the extraction of one kg of lithium can avoid up to 20 tons of CO 2 e emissions with intensive recycling. These benefits come mainly from the higher energy efficiency and energy source diversification of EVs. However, EVs have higher metal and critical mineral requirements driven by LIB production, which will require the expansion of mining supply chains and policy actions to proactively mitigate their environmental impacts. The stock nature of metals and minerals allows for their recovery at end-of-life with the adequate recycling infrastructure, thus extending the climate benefits over time and reducing reliance on materials imports. Overall, our work highlights the handprint benefits of LIB production and lithium extraction in substituting our current fossil fuel energy system for light-duty transportation. Declarations Conflict of Interest Statement: The authors declare no conflict of interest Data Availability Statement: Data used to support the findings of this study were retrieved from the following resources available in the public domain: GREET (https://greet.anl.gov/publications), EIA Annual Energy Outlook 2025 (https://www.eia.gov/outlooks/aeo/tables_ref.php), NREL Cambium (https://www.nrel.gov/analysis/cambium) and TEDB (https://tedb.ornl.gov/). Other data used to support the findings of this study are subject to third-party restrictions: ICCT roadmap, EV Volumes and ecoinvent 3.11. 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14:38:01","extension":"html","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":150685,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8436282/v1/1197bf670b720a94e82ec0eb.html"},{"id":99812784,"identity":"4523e4b8-5cb4-4caf-b304-73e0e4d58e9d","added_by":"auto","created_at":"2026-01-08 14:37:50","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":235085,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eModel diagram and data sources\u003c/strong\u003e. A fleet-wide (2025-2050) LCA is performed for forecasted EV sales in the US, which is compared to a counterfactual of ICEV sales instead of EV. GREET, Greenhouse gases, Regulated Emissions, and Energy use in Technologies model; ICCT, International Council on Clean Transportation; EIA, US Energy Information Administration; AEO, Annual Energy Outlook; TEDB, Transportation Energy Data Book.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8436282/v1/3c346d9f24e2e7482e79f106.png"},{"id":99812932,"identity":"ec77ac54-d578-433d-aab2-d72f8d3ad837","added_by":"auto","created_at":"2026-01-08 14:38:11","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":107832,"visible":true,"origin":"","legend":"\u003cp\u003ePrimary energy consumption, in TWh, for entire fleet (2025-2050) comparison between an EV fleet versus a counterfactual ICEV fleet. Top axis shows average energy consumption, in kWh per kilometer driven.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8436282/v1/ff082d75efa9566f02e40fbd.png"},{"id":99812512,"identity":"e940e737-6b99-48fe-8b21-93bf2dc03778","added_by":"auto","created_at":"2026-01-08 14:37:15","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":217759,"visible":true,"origin":"","legend":"\u003cp\u003eMaterials requirements for entire fleet (2025-2050) comparison between an EV fleet versus a counterfactual ICEV fleet. Right axis shows average material consumption, in kg per kilometer driven. Panels show detail for fossil energy material, metals and critical minerals consumption (for 0% and 80% recycling). REE: Rare earth elements.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8436282/v1/d222a750f012299981da0e5b.png"},{"id":99812577,"identity":"a0c5be53-606e-4786-94fe-88638e5680be","added_by":"auto","created_at":"2026-01-08 14:37:27","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":77983,"visible":true,"origin":"","legend":"\u003cp\u003eAnnual GHG emissions comparison between an EV fleet versus a counterfactual ICEV fleet over time (\u003cstrong\u003ea\u003c/strong\u003e) and cumulated (\u003cstrong\u003eb\u003c/strong\u003e). Color range bar shows emissions under different scenarios for fuel efficiency improvements for ICEVs, LIB lifetime variability, LIB capacity reductions or increases, and different electricity grid forecast scenarios. Top axis shows the average carbon emissions in grams CO\u003csub\u003e2\u003c/sub\u003ee per kilometer driven.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8436282/v1/acbdd2164ce1d17811fa5841.png"},{"id":99812645,"identity":"99350b63-2eea-439e-9df3-7a2886b51109","added_by":"auto","created_at":"2026-01-08 14:37:32","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":168425,"visible":true,"origin":"","legend":"\u003cp\u003eSensitivity analysis for GHG emissions avoided due to LIB (\u003cstrong\u003ea\u003c/strong\u003e) and lithium (\u003cstrong\u003eb\u003c/strong\u003e) usage in EVs. Average for the whole EV and counterfactual ICEV fleet comparison (2025-2050). Big panels show scenarios for LIB lifetime (columns) and lithium recycling rates (rows). Each panel shows scenarios for LIB average capacity (columns) and different electricity grid projections (rows).\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-8436282/v1/bece6742686eafd292864e00.png"},{"id":99815967,"identity":"b7f32f4e-4b84-480d-a10c-5dfd6bdc5e42","added_by":"auto","created_at":"2026-01-08 14:45:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1347504,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8436282/v1/76881423-1d0d-436d-9ce7-b6ee294f7392.pdf"},{"id":99812944,"identity":"fb3d2bfd-c999-4b30-ae76-5a150590fb75","added_by":"auto","created_at":"2026-01-08 14:38:13","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":1313445,"visible":true,"origin":"","legend":"","description":"","filename":"SIManuscript.docx","url":"https://assets-eu.researchsquare.com/files/rs-8436282/v1/a825641080926b7250307759.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"The energy, material and carbon handprint of lithium-ion batteries in electric vehicles","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cp\u003eThe electrification of road transportation is crucial for reducing greenhouse gas (GHG) emissions from the transport sector. Battery electric vehicles (EVs) powered by lithium-ion batteries (LIBs), emit lower GHG and tailpipe pollution per kilometer driven than internal combustion engine vehicle (ICEVs), but have higher impacts in their manufacturing supply chains (Hawkins et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2012\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Lombardi et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Verma et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Adoption of EVs has grown significantly; in the United States (US) sales exceeded 1.5\u0026nbsp;million units in 2024, reaching 10% of all new light-duty vehicle sales, with battery electric vehicles (BEVs) making up around 80% of those sales and plug-in hybrid electric vehicles (PHEVs) making up the balance (ICCT, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). EV sales are expected to keep growing, increasing the demand for critical minerals contained in the LIBs, particularly lithium, cobalt, and nickel (IEA, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), which raises concerns about the life cycle environmental impacts, both global and local, of vehicle electrification (Bouter \u0026amp; Guichet, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Dolganova et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePrevious life cycle assessment (LCA) studies on road electrification, and especially those studies focusing on LIBs in EVs, have primarily focused on quantifying the environmental burdens associated with LIB manufacturing, even when EVs and ICEVs are examined in a comparative context (Hawkins et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Lombardi et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Onat et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Verma et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). At the battery system level, studies have quantified GHG emissions across different LIB chemistries (Ambrose \u0026amp; Kendall, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Bouter \u0026amp; Guichet, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Lai et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) or across various LIB recycling technologies (Dunn et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). At the vehicle level, multiple studies have compared EVs and ICEVs, finding that EVs generate lower life cycle GHG emissions per kilometer traveled under nearly all conditions considered (Lombardiet al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Woody et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2022\u003c/span\u003ea). The carbon intensity of the electricity grid is a key determinant of the climate benefit of EVs, as in regions with high-emission grids the advantage of EVs may be reduced or even outweighed, particularly when compared to high-efficiency gasoline hybrid electric vehicles (HEVs) (Singh et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Other factors, including vehicle size, ambient temperature, battery degradation, charging schedule, and driving patterns, also influence GHG vehicle emissions (Ambrose et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Archsmith et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Onat et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Singh et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Woody et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eProduct-level LCA studies focus on the environmental burdens of individual and specific LIBs and EVs, providing valuable insights into material- and technology-level comparisons, but offering limited insight on the system-wide implications of a large-scale transition to electric mobility and the system-level benefits that LIB production and mineral extraction can enable. Past fleet-level assessments omit the role of critical minerals in enabling the climate benefits of EVs. Although several modeling studies have projected future demand and supply for critical materials, such as lithium under various electrification scenarios (Busch et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Woodley et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), few studies have quantified how the extraction of these minerals translates into environmental benefits across the fleet, or analyze how battery downsizing or circular economy actions may increase the environmental benefits per kg of mineral extracted. There is a need to evaluate the broader sustainability implications of large-scale EV deployment, and how effectively LIBs and mineral extraction are contributing to climate mitigation through electrification.\u003c/p\u003e \u003cp\u003eThe concept of a \u0026ldquo;handprint\u0026rdquo; was introduced in 2007 to describe the beneficial environmental outcomes of an action (CEE, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Norris \u0026amp; Phansey (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) later defined a handprint as a footprint-consistent estimate of the positive changes resulting from an intervention. This concept has since been incorporated into LCA studies and is best evaluated using a system-level scope. In recent literature, handprints are typically represented as avoided impacts relative to a reference scenario, emphasizing their role in quantifying positive climate outcomes (Alvarenga et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this study, we apply the handprint concept within a comparative LCA framework to evaluate the climate benefits associated with LIB manufacturing and lithium extraction for EV deployment. Our scope is the projected EV fleet in the US from 2025 to 2050, and the counter-factual comparison scenario of an all-ICEV future instead. The novelty of this work includes a system-level scope that includes the whole US light-duty fleet, using updated data sources for EV sales projections, battery size and chemistry, electricity grid forecasts and evaluation of the life cycle energy, material and carbon intensity of both futures. The fleet level model allows us to determine the climate benefits of LIBs and lithium, and how different actions, such as lifetime extension and recycling, can increase their climate benefits over time.\u003c/p\u003e"},{"header":"2. METHODS","content":"\u003cp\u003eWe conducted a comparative LCA for the whole fleet of the US from 2025 to 2050, contrasting a 100% EV adoption scenario with an ICEV-only counterfactual scenario. We omit from both scenarios the sales and stock of baseline ICEV, hybrids and hydrogen-powered vehicles, and thus we are only comparing the effects of projected EV sales and projected ICEV sales under the two modeled scenarios. We refer here to EVs as vehicles powered solely by traction LIBs, as they constitute the majority of future electric vehicle sales (ICCT, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), thus omitting all powertrains, even PHEVs, that have gasoline engines. The LCA scope includes the stages of vehicle and battery manufacturing, vehicle maintenance; driving; vehicle and battery turnover based on survival curves; carbon intensity from the electricity grid based on energy mix forecasts; and end of life management of batteries and vehicles (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e for full data sources details).\u003c/p\u003e \u003cp\u003eThe main metric of comparison is GHG emissions expressed in units of CO\u003csub\u003e2\u003c/sub\u003e-equivalent calculated using 100-year global warming potential (GWP) factors that exclude land use and biogenic emissions (IPCC, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). We also quantify primary net energy consumption and material usage (non-renewable elements), as well as applying TRACI impact assessment methods for ozone depletion, photochemical oxidation, air acidification and human health particulate air pollution emissions (EPA, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). For GWP, we also tested time-adjusted global warming potentials (TAWPs) for a 100-year period, to account for differences in emissions timing between EVs and ICEVs (Kendall, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Fleet Stock\u003c/h2\u003e \u003cp\u003eWe estimate vehicle stocks using a fleet model based on a forecast of vehicle sales and vehicle and battery survival curves, at geographic resolution of each US state (\u003cem\u003es\u003c/em\u003e), as described in Eq.\u0026nbsp;\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, where \u003cem\u003et\u003c/em\u003e refers to years, \u003cem\u003es\u003c/em\u003e refers to states (location), and \u003cem\u003eage\u003c/em\u003e refers to vehicles of a given age within the fleet:\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:Flee{t}_{t,s}=Flee{t}_{t-1,s}+Sale{s}_{t,s}-\\sum\\:_{age=0}^{30}\\left(Flee{t}_{t,s,age}*Vehicle\\:Failure\\:fractio{n}_{age}\\right)$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eTo build the stock model, historical light-duty EV sales (starting from 2016) are taken from EV Volumes (J.D. Power, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Light-duty EV sales forecasts for the US are drawn from the International Council on Clean Transportation (ICCT) Roadmap v2.6 \u0026ldquo;Ambitious\u0026rdquo; scenario (ICCT, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The Ambitious scenario assumes 100% light-duty EV sales by 2035 for the US. EV Volumes and the ICCT Roadmap report sales on a national scale. We disaggregated them to state level following historical EV registration records (DOE, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) and using a liner interpolation towards population share by state in 2035 (U.S. Census Bureau, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) (SI Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Sales were disaggregated into vehicle type (cars and light-trucks) by region following the annual energy outlook (AEO) estimates of LDV fleet composition (EIA, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2025b\u003c/span\u003e) (SI Figure S2).\u003c/p\u003e \u003cp\u003eWe consider that vehicles and batteries can fail independently following the product-component framework (Aguilar Lopez et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). We follow previous lifetime assumptions for EVs and LIBs (Busch et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), both following a logistic distribution with mean 17 years for vehicles and 15 years for LIBs, with a 4-year standard deviation. LIB failure in a working vehicle generates inflows (replacement) and outflows of LIBs (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). We consider that a vehicle younger than 8 years will get a new battery covered by the warranty, and that a vehicle above 12 years will be scrapped if their battery fails. If available, a vehicle aged 8 to 12 may get a battery in good condition (coming from a vehicle-only failure) or a new LIB. We consider that 25% of batteries in good condition coming from vehicle failures are available to be reused in other vehicles, while the rest of batteries go towards recycling. For the counterfactual of ICEV, the same lifetime distribution is assumed for vehicles.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Manufacturing\u003c/h2\u003e \u003cp\u003eManufacturing impacts are estimated by multiplying the required vehicles and batteries (new and for replacement) with their upstream unitary impacts. We use upstream life cycle inventory (LCI) data primarily sourced from the ecoinvent 3.11 database (SI Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e lists the LCI datasets used). For modeling the LCA of vehicle manufacturing we distinguish vehicle types (car and light-trucks) based on their different curb weight (Argonne National Laboratory, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). For BEVs we include battery production upstream impacts combined with battery capacity estimates as a function of vehicle type and current cathode chemistries in the US (SI Figure S3) (J.D. Power, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The data sources for vehicle sales and LIB replacements are explained in detail in the Fleet Stock section.\u003c/p\u003e \u003cp\u003eFor impacts associated with vehicle and LIB manufacturing we consider an amortization (negative impact due to remaining lifetime) at year 2050, assuming a vehicle and LIB working lifetime of 15 years. For example, for a vehicle that was produced in 2040, we subtract one third of its manufacturing impact in 2050 to account that the vehicle still has remaining working lifetime.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Vehicle Driving\u003c/h2\u003e \u003cp\u003eWe estimate total driving energy consumption, in electricity or gasoline, by multiplying the vehicle fleet by age at each year (SI Figure S4) by their annual vehicle miles traveled (VMT) (SI Figure S5) and vehicle energy consumption per mile (SI Figure S6), both parameters dependent on vehicle age (see Eq.\u0026nbsp;\u003cspan refid=\"Equ2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\:Energy\\:con{s}_{t,s}\\:\\left[kWh\\:or\\:liters\\:gas\\right]=\\sum\\:_{age=0}^{30}\\left(Flee{t}_{t,s,age}*VM{T}_{age}*mpg{e}_{t,age}\\right)$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eEnergy consumption data is from Environmental Protection Agency (EPA 2-cycles), so we adjust them to reflect real world driving conditions and differences in weather across the US. We use temperature adjustment factors, accounting for both warm and cold conditions, at county level, provided by (Woody et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), differentiated for EVs and ICEVs. We consider an efficiency degradation by age of the vehicle 1% per year for ICEVs and 1/3% per year for EVs (Olgu\u0026iacute;n et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). EV charging losses are already embedded in the energy consumption factors (EIA, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2025b\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eThe impact per kWh of electricity consumed in an EV depends on the electricity generation method and their upstream impacts. We use the electricity generation forecast by EIA for all the contiguous US states, and EPA eGRID for Hawaii and Alaska to estimate the future grid mix per energy market module region (SI Figure S7). Electricity generation impact per energy source was obtained from ecoinvent 3.11, for different US regions (SI Table S2). Electricity balancing regions in the US do not necessarily conform to county boundaries. We combine spatial polygon data layers of electricity balancing regions and counties by assigning each county a specific region based on the largest area overlap, and then we estimate the state average grid mix using county population as weights. We include 5% transmission and distribution losses (EIA, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Overall, the US grid will reduce its carbon intensity by 2050 (SI Figure S8) due to higher renewable energy adoption. Gasoline combustion impact was estimated using total gasoline consumption with associated combustion and gasoline production emissions (e.g., oil extraction and refining), obtained from ecoinvent 3.11.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Recycling\u003c/h2\u003e \u003cp\u003eWe consider that all vehicles are recycled at end-of-life, with the associated LCI impacts of the recycling process. We use the following assumptions for material recovery: steel (96% recovery), aluminum (91%), lead (99%), nickel (80%), magnesium (70%) and copper (50%) (Argonne National Laboratory, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Gl\u0026ouml;ser et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Kumar et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). We consider battery recycling at different recycling collection levels, assuming recovery rates of 90% for nickel, cobalt and lead; 80% for lithium; and 95% for copper, rare earth elements (REE), manganese and aluminum (European Parliament, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFor material usage results we assume that the recovery of these metals avoids new mineral extraction. From a life cycle accounting perspective, this means we credit recycling with avoided lithium extraction impacts assuming an equal mix of lithium production from hard-rock and concentrated brines.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Carbon Handprint\u003c/h2\u003e \u003cp\u003eWe estimated the carbon handprint of LIBs and lithium as the avoided GHG emissions, in units of CO\u003csub\u003e2\u003c/sub\u003ee, due to the adoption of EVs instead of ICEV. The handprint for LIBs is estimated per kWh of battery storage manufactured:\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$$\\:LIB\\:Handprint=\\frac{{\\sum\\:}_{t}(GH{G}_{EV\\:Fleet}-GH{G}_{ICEV\\:Fleet}\\left)\\right[tons\\:C{O}_{2}e]}{{\\sum\\:}_{t}LIB\\:Requirement\\:\\left[kWh\\right]\\:}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eIn a similar way, the handprint of lithium can be estimated per kg of lithium extracted:\u003cdiv id=\"Equ4\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ4\" name=\"EquationSource\"\u003e\n$$\\:Lithium\\:Handprint=\\frac{{\\sum\\:}_{t}(GH{G}_{EV\\:Fleet}-GH{G}_{ICEV\\:Fleet}\\left)\\right[tons\\:C{O}_{2}e]}{{\\sum\\:}_{t}Lithium\\:Extraction\\:\\left[kg\\right]\\:}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e4\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWe tested different parameters for the carbon handprint of LIBs and lithium. For the electricity grid, besides the \u0026ldquo;2025 reference\u0026rdquo; scenario, we consider the EIA extreme scenarios in fossil fuel adoption of \u0026ldquo;low oil \u0026amp; gas supply\u0026rdquo; and \u0026ldquo;high oil \u0026amp; gas supply\u0026rdquo;; along with using the forecasted mix by the Cambium model (EIA, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2025b\u003c/span\u003e; NREL, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). We tested a short battery lifetime scenario with 10 years average lifetime, and a long battery lifetime scenario with 20 years. We use the 95th quantile of battery size to construct scenarios on battery capacity (J.D. Power, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), using 68 kWh and 83 kWh for cars and light trucks in a low-capacity scenario, and 100 kWh for cars and light trucks in the high-capacity scenario. For energy consumption in ICEVs, we consider a scenario with a 15% linear improvement in miles per gallon (mpg) towards 2040 (SI Figure S6). Finally, we consider different LIB recycling collection rates: 0%, 40% and 80%.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. RESULTS","content":"\u003cp\u003eOn a life cycle basis, the EV fleet has lower energy consumption, material extraction and GHG emissions than a counterfactual ICEV fleet (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePrimary energy consumption, material extraction and GHG emissions from the US future EV fleet (2025\u0026ndash;2050) and a counter-factual ICEV fleet.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetric\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEVs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eICEVs\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePrimary Energy, TWh\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e71,155\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e89,252\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5,952\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e69,647\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15,914\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8,655\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNatural Gas\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17,403\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8,244\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUranium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11,861\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,278\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRenewable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19,654\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,416\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e370\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMaterials, million tons\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e5,445\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e8,264\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFossil Fuels\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4,521\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7,922\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Metal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e203\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e721\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e332\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCritical Minerals\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e230\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e82\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGHG, million tons CO\u003c/b\u003e\u003csub\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sub\u003e\u003cb\u003ee\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e9,690\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e24,893\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Primary energy consumption\u003c/h2\u003e \u003cp\u003eA future all-EV fleet will require 20% less primary energy consumption than a counterfactual ICEV fleet. The higher battery-to-wheel efficiency (85%-90%) from EVs (Ezugwu et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Gustafsson \u0026amp; Johansson, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Weiss et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) more than compensates for the additional energy consumed in LIB manufacturing and energy losses in electricity production, distribution and charging (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). On a life cycle basis, ICEVs will consume 1.25 kWh per km driven on average and EVs consume 1 kWh per kilometer driven. However, EVs consume 42% less non-renewable energy than ICEVs, because of renewable energy resources on US grids.\u003c/p\u003e \u003cp\u003eAt fleet level, the reduction translates to major savings in energy consumption: the counterfactual future of an ICEV fleet will require the consumption of 70,000 TWh of oil (equivalent to 41,000\u0026nbsp;million oil barrels), along with consumption of 8,500 TWh coal (vehicle production mainly) and 8,000 TWh of natural gas (vehicle production and oil refining). The EV adoption scenario will require the consumption of non-renewable primary energy for electricity generation and vehicle (including LIB) manufacturing: 16,000 TWh of coal, 17,000 TWh of natural gas, 6,000 TWh of crude oil and 12,000 TWh of uranium (nuclear energy).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe majority (81%) of energy consumption for ICEV occurs at the driving stage, associated with gasoline consumption, while for EVs driving accounts for 55% of total primary energy consumption, with vehicle production consuming 22% and LIB production 23%. The latter shows that further decarbonization of EV supply chains, especially LIB manufacturing, could have a substantive effect on reducing life cycle energy consumption.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Material consumption\u003c/h2\u003e \u003cp\u003eICEVs has a 52% higher material consumption than EV, mostly driven by the fossil fuel extraction requirements (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). This result challenges the common claim that EVs are more material intensive than ICEVs, and reinforces the claim that the energy transition will have less mining requirements than our current fossil fuel system (Nijnens et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Moreover, fossil energy materials are not recoverable after use, eliminating the possibility for circularity and future reductions in primary material extraction.\u003c/p\u003e \u003cp\u003eThe counter-factual future of an ICEV fleet will require the extraction of 1,400\u0026nbsp;million tons of coal, 6,000\u0026nbsp;million tons of crude oil and 800\u0026nbsp;billion m\u003csup\u003e3\u003c/sup\u003e of natural gas, while the EV fleet scenario will require 2,500\u0026nbsp;million tons of coal (86% more), 500\u0026nbsp;million tons of oil (92% less), and 1,800\u0026nbsp;billion m\u003csup\u003e3\u003c/sup\u003e of natural gas (110% more). Given the high energy density of uranium in nuclear energy generation, the EV scenario will only require 76,000 tons of uranium.\u003c/p\u003e \u003cp\u003eThe EV fleet has 117% higher metal consumption than ICEV, mainly driven by the additional material requirements in LIB manufacturing (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The vast majority of additional metal requirement is iron for steel production, which can be recovered at end-of-life at high rates (96%) (Argonne National Laboratory, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Our results also omit improvement in LIB manufacturing efficiencies or changing cathode chemistries which could decrease metal intensity per kWh manufactured.\u003c/p\u003e \u003cp\u003eIn terms of critical minerals, as defined by the USGS (Nassar et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), EVs have a 179% higher (or 126% in the high recycling scenario) requirement than ICEV, mostly from higher copper, silicon and aluminum usage in LIBs. The future EV fleet will require the manufacturing of 34 TWh of LIBs with the associated extraction of 1.4, 15 and 0.9\u0026nbsp;million tons of lithium, nickel, cobalt, respectively. While the consumption of critical minerals is higher in EVs, they can recover at LIB end-of-life thus reducing the amount of primary material extraction to 0.5, 10 and 0.5 for lithium, nickel and cobalt under a high recycling assumption. If we consider the total service provided by vehicles in kilometers driven, the consumption of metals in EVs is in the order of magnitude of 1 kg per 100 km driven, and for critical minerals is around 1 kg per 315 km driven. EVs require 1 kg of lithium per 50,000 kilometers driven (or per 80,000 km under high recycling), 1 kg of nickel per 5,000 km (7,000 with recycling), and 1kg of cobalt per 80,000 km (135,000 with recycling).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Carbon emissions\u003c/h2\u003e \u003cp\u003eAs a direct consequence of lower non-renewable primary energy consumption, the carbon footprint of an EV fleet is around 135 gCO\u003csub\u003e2\u003c/sub\u003ee per km, a 61% reduction from the footprint of 346 gCO\u003csub\u003e2\u003c/sub\u003ee from an ICEV fleet (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). In absolute terms, the future EV fleet for the US will generate 9,500\u0026nbsp;million tons of CO\u003csub\u003e2\u003c/sub\u003ee, and the ICEV counter-factual fleet generates 25,000\u0026nbsp;million tons. The stage disaggregation of GHG emissions follows non-renewable primary energy usage (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), where driving constitutes the majority of emissions for ICEV (85%), while for BEVs it is equally divided into vehicle production (35%), LIB production (31%) and driving (34%). Even with improvements in efficiency for the future ICEV fleet and in the large emissions from US EV manufacturing due to battery size, durability or electricity for EVs, the difference in emissions remains strongly in favor of EVs, mostly due to lower emissions on the driving usage and the barriers to abating combustion emissions from gasoline.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eUsing upstream GHG emission factors for vehicle and LIB manufacturing from the GREET model further increases the difference in the carbon footprint between an EV and a counterfactual ICEV fleet (Argonne National Laboratory, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), with the EV fleet having 69% fewer GHG emissions, and an average emission of 92 gCO\u003csub\u003e2\u003c/sub\u003ee per km (SI Figure S9). The GREET model assumes fewer GHG emissions in LIB manufacturing, so the majority of emissions come from driving (50%), vehicle manufacturing (30%) and LIB production (20%). Assuming long-run marginal electricity emissions factors provided by the Cambium model (NREL, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), instead of average grid mix, increases the driving emissions from EVs, but on a life cycle basis they still have 55% fewer emissions than an ICEV fleet (SI Figure S10).\u003c/p\u003e \u003cp\u003eThe timing of emissions is different for both scenarios, with EVs having higher upfront emissions due to manufacturing requirements that are sustained over time and lower driving emissions as the electricity grid gets cleaner, and ICEVs having increasing driving emissions over time due to driving efficiency degradation. The timing of GHG emissions or removals has a demonstrative effect on their global warming impact, which is not captured in typical carbon accounting methods and global warming potential. Brand\u0026atilde;o et al. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) offer a review of alternative methods, including those that retain the unit of CO\u003csub\u003e2\u003c/sub\u003e-equivalent for compatibility with traditional LCA methods. Here we use one of those methods, Time Adjusted Warming Potentials (TAWPs), which results in a unit of CO\u003csub\u003e2\u003c/sub\u003e-equivalent emitted today (Kendall, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). After applying this method, we find that the relative difference between EV and ICEV futures remains essentially unchanged from the case where traditional GWPs are used, meaning that emissions timing is not significant for this assessment (SI Figure S11).\u003c/p\u003e \u003cp\u003eFor other impact categories, EVs have 30% higher emissions of SO\u003csub\u003e2\u003c/sub\u003eeq (acidification), 65% higher emissions of PM\u003csub\u003e2.5\u003c/sub\u003eeq (human health), 27% higher emissions of O\u003csub\u003e3\u003c/sub\u003eeq (smog formation) and 71% lower emissions of CFC\u003csub\u003e11\u003c/sub\u003eeq (ozone depletion) (SI Figure S12). The higher impact for acidification, human health particulate matter and smog formation is driven mainly by LIB production. The total damage resulting from these emissions will depend on the location of the impact, so more detailed spatial LCA methods are needed to assess the local environmental damage of the EV supply chain.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Carbon handprint of lithium and LIBs\u003c/h2\u003e \u003cp\u003eEVs have lower carbon emissions than their ICEV counterparts (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), meaning that LIB and lithium act as technology enablers to generate a net positive climate effect. We estimate the carbon handprint of LIB production and primary lithium extraction as the total emissions avoided resulting from both fleet comparisons (EV vs ICEV, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The handprint already accounts for LIB manufacturing and lithium extraction emissions. Each kWh of LIB manufactured for an EV avoids 450 kg of CO\u003csub\u003e2\u003c/sub\u003ee, moreover each kilogram of primary lithium extracted avoids 11 tons of CO\u003csub\u003e2\u003c/sub\u003ee (assigning all reductions to lithium, and none to other critical minerals in the LIB, such as nickel, cobalt, graphite or copper). In a scenario with shorter lifetimes and bigger LIBs, the handprint is still around 250 kg CO\u003csub\u003e2\u003c/sub\u003ee avoided per kWh of battery and can be as high as 650 kg CO\u003csub\u003e2\u003c/sub\u003ee per avoided kWh in a scenario with long battery life and smaller LIBs. Similarly, the lithium handprint with no recycling can be as low as 5-ton CO\u003csub\u003e2\u003c/sub\u003ee avoided/kg Li and as high as 12-ton CO\u003csub\u003e2\u003c/sub\u003ee avoided per kg of Li, depending on the battery size and durability assumptions. Notably, the forecasted grid electricity has a relative minor effect on the handprint of LIBs and lithium.\u003c/p\u003e \u003cp\u003eLithium can be recovered at end-of-life from LIBs, thus reducing the amount of virgin lithium extraction and increasing the climate benefits of lithium (as it can be used in multiple batteries). The different recycling levels have the biggest effect in the lithium handprint, with a potential benefit over 23 tons of CO\u003csub\u003e2\u003c/sub\u003ee avoided per kg of primary lithium extracted in an 80% recycling scenario (64% net recovery, as we assume 80% of lithium is recovered in the hydrometallurgical process).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. DISCUSSION","content":"\u003cp\u003eA nation-wide light-duty fleet level comparison allows us to analyze the system change in carbon emissions, material usage and energy consumption for the US in the next 25 years (2025\u0026ndash;2050).\u003c/p\u003e \u003cp\u003eIn energy terms, EVs provide two main advantages over ICEVs: higher overall efficiency from well-to-wheel and diversification of primary energy sources. We find that EVs consume 20% less primary energy than ICEV, even after accounting for LIB manufacturing and losses in electricity generation, transmission and distribution. The main reason is due to the high battery-to-wheel efficiency (85%-90%) (Ezugwu et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), while ICEV vehicle tank-to-wheel efficiency is lower (14%-33%) (Albatayneh et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The energy supply of ICEV is mainly crude oil for gasoline, and this concentration leads to geopolitical risks and import-dependence (Cheng et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The energy inputs for EVs are much more diversified coming mainly from coal for vehicle production, and natural gas, uranium and a growing share of renewable sources for electricity generation. Further advancements in cleaning the electricity grid will further diversify their energy source and decarbonize EVs in all life cycle stages.\u003c/p\u003e \u003cp\u003eEVs have a 34% lower material footprint than ICEVs, mostly driven by the high fossil fuel material extraction needed to sustain the lifetime operation of an ICEV. However, EVs have a 117% higher metal and 179% higher critical minerals footprint, mainly due to higher material usage in the manufacturing of LIBs. We found that EVs consume much more iron than ICEV, a high-grade ore mineral (around 60%) (Tuck et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and that the emerging LIB manufacturing supply chains could become more efficient over time in reducing material scrappage and increasing recovery. For critical minerals, EVs consume much more copper, aluminum, nickel, cobalt and lithium, generating potential risks and environmental impacts on the expanding mining supply chains. The higher critical mineral intensity of EVs is an important area to improve by demand-side actions such as increasing LIB longevity, improving energy efficiency, expanding the charging infrastructure and exploring new LIB chemistries (Busch et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Metals, as opposed to fossil fuels, can be recovered and re-introduced to the economy with recycling, thus creating a secondary source of minerals and preventing virgin material extraction. However, the fast adoption of EVs will require massive extraction in the next 15 years until mineral stock is built into the economy and most LIBs start becoming available for recycling (J. Dunnet al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eEVs have 61% lower GHG emissions than ICEV, mainly from their lower energy consumption and their higher share of low-carbon energy sources (solar, wind, hydro and nuclear). The difference remains even by accounting for different scenarios and timing of emissions. Moreover, EVs have a direct pathway to keep reducing their carbon footprint. The majority of ICEV emissions come from gasoline combustion, which could only be partially mitigated through efficiency improvements or with biofuel substitutions, which may entail heavy land-use changes impacts. Our results indicate that life cycle carbon emissions of EVs come from three equally proportional stages: vehicle manufacturing, LIB production and driving; and all these stages will benefit from improvements in global electricity grid emissions (as manufacturing occurs outside the US). Reductions in material usage, such as lightweighting or better manufacturing process, will also generate carbon reductions (Wiedenhofer et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMultiple studies have highlighted lithium extraction and LIB manufacturing environmental impacts, with less attention to their climate benefits in decarbonizing the light-duty transport sector. We found that LIB production and lithium extraction enables major life-cycle carbon reductions in EVs, in the order of 0.3 to 0.6 tons CO\u003csub\u003e2\u003c/sub\u003ee avoided per kWh of LIB and 5 to 23 tons CO\u003csub\u003e2\u003c/sub\u003ee avoided per kg of lithium extracted. The latter assumes full allocation of the benefits to lithium, while other metals are required in LIBs, but it illustrates that mining for critical minerals will provide multiple climate benefits that can be sustained over time, especially with circular economy actions as most battery minerals can be recycled back into cathode grade materials. Mining does generate major environmental impacts (Giljum et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), such as water consumption (Islam et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), land-use change and deforestation (Mervine et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). A priority for the energy transition should be to mitigate mining impacts through demand-side actions to reduce minerals requirements and prevent unnecessary mine openings (Busch et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The recovery of minerals through recycling offers multiple benefits such as lower energy requirements, less ecosystems disruption and a long-term domestic supply for minerals. While we have demonstrated the lower energy, material and carbon impacts of EVs with respect to ICEV, they do have higher metal consumption and higher impacts on acidification, human health (particulate matter) and smog formation that should be actively managed to reduce damage to humans and ecosystems.\u003c/p\u003e \u003cp\u003eOur analysis has multiple limitations that span directions for future research. In the modelling aspect, we consider the same lifetime of EVs and LIB for all states in the US and all LIB chemistries, even as factors like climate may alter the durability of each component. To characterize manufacturing impacts, we rely on static LCI background data with no efficiency improvements, thus resulting in a likely overestimation of impacts for the manufacturing stage. This is especially relevant for EVs, as current LIB manufacturing has a high energy and material usage that has the potential to improve with manufacturing advances and cleaner electricity grids in the major producing countries, like China. Our counterfactual scenario only considers EV and ICEV, omitting hybrid vehicles. We consider only one scenario of number of EV sales, ignoring that vehicle production can be prevented with better access to repair, or reductions in vehicle ownership per capita with public transit or high-occupancy vehicle rides. These are policy options that will reduce the energy, material and carbon impacts of the light-duty transportation sector by providing the same mobility benefits. For electricity emissions we are using the average grid mix, omitting the construction of transmission and distribution infrastructure to meet the growing power demand for charging electric vehicles. For the material usage impact, we are not counting removed ore due to mining, which can generate higher mass removals especially for metals with low ore grades (Wang et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e"},{"header":"5. CONCLUSION","content":"\u003cp\u003eWe have shown that the future US EV light-duty fleet will reduce the primary energy consumption, material usage and GHG emissions compared to a counterfactual ICEV fleet. The manufacturing of one kWh of LIB can avoid up to 600 kg of CO\u003csub\u003e2\u003c/sub\u003ee emissions, and the extraction of one kg of lithium can avoid up to 20 tons of CO\u003csub\u003e2\u003c/sub\u003ee emissions with intensive recycling. These benefits come mainly from the higher energy efficiency and energy source diversification of EVs. However, EVs have higher metal and critical mineral requirements driven by LIB production, which will require the expansion of mining supply chains and policy actions to proactively mitigate their environmental impacts. The stock nature of metals and minerals allows for their recovery at end-of-life with the adequate recycling infrastructure, thus extending the climate benefits over time and reducing reliance on materials imports. Overall, our work highlights the handprint benefits of LIB production and lithium extraction in substituting our current fossil fuel energy system for light-duty transportation.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConflict of Interest Statement:\u0026nbsp;\u003c/strong\u003eThe authors declare no conflict of interest\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement:\u0026nbsp;\u003c/strong\u003eData used to support the findings of this study were retrieved from the following resources available in the public domain: GREET (https://greet.anl.gov/publications), EIA Annual Energy Outlook 2025 (https://www.eia.gov/outlooks/aeo/tables_ref.php), NREL Cambium (https://www.nrel.gov/analysis/cambium) and TEDB (https://tedb.ornl.gov/). Other data used to support the findings of this study are subject to third-party restrictions: ICCT roadmap, EV Volumes and ecoinvent 3.11. All data inputs and code required to reproduce the model results are publicly available in the following repository: https://github.com/pmbusch/USA-EV-Lithium-GHG.\u003c/p\u003e\n\u003cp\u003eACKNOWLEDGMENTS\u003c/p\u003e\n\u003cp\u003eThe authors acknowledge and thank ICCT for sharing detailed information on their Roadmap model.\u003c/p\u003e\n\u003cp\u003eFUNDING INFORMATION\u003c/p\u003e\n\u003cp\u003eThis work was funded by grants from the Heising-Simons Foundation (grant no. 2023-4360 to A.K.) and ClimateWorks Foundation (grant no. G-2308-802319017 to A.K.).\u003c/p\u003e\n\u003cp\u003eCOMPETING INTERESTS\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAguilar Lopez, F., Billy, R. G., \u0026amp; M\u0026uuml;ller, D. B. (2022). 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The role of pickup truck electrification in the decarbonization of light-duty vehicles. \u003cem\u003eEnvironmental Research Letters\u003c/em\u003e, \u003cem\u003e17\u003c/em\u003e(3), 034031. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1088/1748-9326/ac7cfc\u003c/span\u003e\u003cspan address=\"10.1088/1748-9326/ac7cfc\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-industrial-ecology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"44498","submissionUrl":"https://submission.springernature.com/new-submission/44498/3","title":"Journal of Industrial Ecology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"electric vehicles, lithium-ion batteries, life cycle assessment, critical minerals, greenhouse gas emissions","lastPublishedDoi":"10.21203/rs.3.rs-8436282/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8436282/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eElectrifying the transport sector will require manufacturing of lithium-ion batteries and extensive mining of their embedded critical minerals, like lithium. Research has quantified the future demand for lithium-ion batteries, their constituent materials and their environmental impacts, but typically without contextualizing these impacts within the benefits of fleet-level decarbonization. We conduct a life cycle assessment of the United States projected light-duty electric vehicle fleet (2025\u0026ndash;2050) and compare it to a counter-factual scenario where all electric vehicles are instead internal combustion engine vehicles to determine the environmental benefits enabled by lithium-ion batteries in electric vehicles. Results show that electric vehicles will reduce primary energy consumption by 20%, material extraction (including fossil fuels) by 34% and carbon dioxide equivalent (CO\u003csub\u003e2\u003c/sub\u003ee) emissions by 61% compared to an internal combustion engine-only future. This translates into 300\u0026ndash;600 kg CO\u003csub\u003e2\u003c/sub\u003ee avoided per kilowatt-hour of lithium-ion battery, or 5\u0026ndash;12 tons CO\u003csub\u003e2\u003c/sub\u003ee avoided per kg of lithium extracted. Under conditions of high battery recycling rates, avoided emissions can increase to 20 tons CO\u003csub\u003e2\u003c/sub\u003ee per kg of lithium. However, electric vehicle deployment increases metal extraction by 117% and critical minerals extraction by 179%. Actions to reduce the metal intensity of EVs are needed such as increasing LIB durability, improving EV energy efficiency, and enhancing battery recycling and metal recovery rates to avoid new mining and multiply the climate benefits of battery mineral extraction.\u003c/p\u003e","manuscriptTitle":"The energy, material and carbon handprint of lithium-ion batteries in electric vehicles","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-08 13:57:08","doi":"10.21203/rs.3.rs-8436282/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-06T11:12:47+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-06T09:00:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"207984172146238770424250459280209558086","date":"2026-01-25T22:53:15+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-23T11:54:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"255949223834533825912928752554079398765","date":"2026-01-19T09:22:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"39741646594126614095496365001389933084","date":"2026-01-09T06:54:14+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-06T15:03:53+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-26T04:47:59+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-26T04:47:36+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Industrial Ecology","date":"2025-12-23T17:37:32+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-industrial-ecology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"44498","submissionUrl":"https://submission.springernature.com/new-submission/44498/3","title":"Journal of Industrial Ecology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"a0b12770-ec43-41f9-ac94-c5e13bd0b7b8","owner":[],"postedDate":"January 8th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-05-19T06:40:56+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-08 13:57:08","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8436282","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8436282","identity":"rs-8436282","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}