Working paper series number 2022/01

Evidence for a global electricvehicle tipping point
Aileen Lam1,2,*, Jean-Francois Mercure2,3,4
1

Department of Economics, Faculty of Social Sciences, University of Macao, E21 Taipa,
Macau, China

2

Cambridge Centre for Environment, Energy and Natural Resources Governance (C- EENRG), Universityof Cambridge; Cambridge UK.
3

Global Systems Institute, Department of Geography, University of Exeter; Exeter, UK.

4

Cambridge Econometrics, Cambridge, UK* Correspondence to: meimeilam@um.edu.moGlobal Systems Institute (GSI)The GSI at the University of Exeter is thought leading in understanding global changes, solving global
challenges, and helping create a flourishing future world together, through transformative research, educationand impact. The GSI aims to become a ‘go to’ place for global change researchers from around the world,bringing them together with industry, policymakers, students and other stakeholders to tackle sharedproblems, and acting as a catalyst that enables translation of this research into applications that deliver
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The GSI is distinctive in uniting a trans-disciplinary group of researchers, educators and partners to lookbeyond single ‘environmental’ issues to a truly systemic view of coupled global changes in the human socialand economic sphere and the biosphere. The Institute builds upon the University’s recognised research
excellence in Earth System Science and Climate Change.
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Evidence for a global electric vehicle tipping point

Abstract
Electric vehicles (EVs) can reduce road transport emissions and have recently seen rapid innovation, decline
in cost and a rise in popularity. Achieving a transition to EVs hinges upon their accessibility to current users
of internal combustion engine vehicles (ICEV). Here we show with historical evidence that globally, an
irreversible private passenger EV diffusion tipping point may have been crossed, where sales of ICEVs
decline in leading markets, as preferences for and access to EVs rise, in a self-reinforcing manner. We
analyse the structure and dynamics between 2016 and 2021 of four leading car markets comprehensively.
The pandemic has drastically affected ICEV sales: many models are now planned to be discontinued, while
EVs see unaffected rapid growth and achieve cost parity within a few years. We suggest that coordinatedpolicy incorporating EV mandates in the leading car markets could induce an EV transition in the rest of theworld.

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1. Introduction
Road transport emissions are rising rapidly, due to growing fleets worldwide, despite efficiency increasing inmost vehicle markets1. Private passenger road transport generates 45% of transport emissions and 30% of
its growth, and thus requires urgent attention from policy-makers2. Since the late 2000s, EVs have been seen
as a major component of the solution for decarbonising private passenger road transport. Meanwhile, plugin hybrid electric vehicles (PHEVs) have been seen as overcoming issues of range anxiety that may haveslowed the diffusion of EVs in some cases3–7. EVs and PHEVs have seen their costs decline drasticallyfollowing large scale investment, anticipating or benefiting from favourable policy environments, in the major
car markets of Europe, North America and China, while discussions are ongoing in India and elsewhere.
Notably, the stock market valuation of Tesla recently exceeded the combined value of its major competitors 8.
In complex systems, a ‘tipping point’ is crossed when a small perturbation transforms the system and leads
to an irreversible change in system trajectory9–11. Technologies often see tipping points in their diffusion
processes where their increased dominance self-reinforces, as costs decline due to increasing investment
anticipating rising sales, and sales increase due to declining costs and improving consumer accessibility10,12–
14. This has occurred before in the 1920s with the onset of oil-based mobility15–17. It could occur with EVs

once ownership costs and the diversity of models available on markets become comparable with incumbent
alternatives. In particular, EVs should ultimately become cheaper to produce and more reliable. Widespread
adoption requires the availability of a diverse set of vehicles that collectively cover the needs of highly
heterogeneous consumers18–21. We hypothesize that a full set of conditions is required for the onset of atipping point.
Costs and technological trajectories are driven by the leading car markets (US, Europe, China, Japan, Korea),
where most innovation occurs. However, to mitigate climate change, emissions reductions are required
worldwide. The affordability of electric mobility in developing countries is a key question that looms over thefeasibility of achieving global emissions targets, agreed in 2015 in Paris at COP21 and re-iterated in 2021 in
Glasgow at COP26. This has led to a proposal and agreement called the ‘Glasgow Breakthroughs Agenda’for the major economies to cooperate on energy innovation to bring down the costs of key technologies andmake them available to the rest of the world10,22–24.
In this paper, we ask whether and when a cost parity point and a diffusion tipping point could be activated forEVs. We explore the diversity of both zero and high-carbon car markets and their evolution over recent years,
including their diversity and cost trajectories, in four major markets (Europe, the US, China and India). We
present a comprehensive analysis of the car market structure between 2016 and 2021 and the data on future
car models from car manufacturers between 2022 and 2026. Using the FTT:Transport model25, we simulate
the diffusion of different vehicle technologies and examine the likely trajectories for costs and rates of uptake
for EVs and ICEVs given historical observations. We then explore the role of different policy instruments,
including notably EV mandates, for eliminating the sales of ICEVs. Using scenario analyses, we explore how
different scenarios of international coordination in implementing EV support policies can bring forward EV
cost parity in other regions or countries.
42. Economic and policy context
The 2020-2022 COVID-19 pandemic has changed car market conditions drastically in comparison to 2019and earlier. Rapid diffusion of teleworking has led to an increased focus towards homes as workplaces,
altering mobility patterns26. Vehicle sales have seen increased volatility, while the oil prices went fromnegative values in 2020 to new highs in 2022. Meanwhile, the cost of batteries, which is the single mostexpensive component of making EVs, has gone down by over 85% since 201027–36. This has been driven byrising investments, notably by Tesla in the US, followed by diverse companies in China and most European
manufacturers. In contrast, the ownership lifetime cost of operating an ICEV, approximately 50% of which isfuel, has not systematically declined; instead cost changes followed the fluctuations in the price of fuel at thepump37. Relevant innovation for ICEVs has focused on increasing energy efficiency but remains limited1.
Debate has emerged on how social tipping points could develop or be achieved to accelerate emissions
reductions, focusing on self-reinforcing social feedbacks9,10,38–40. Here, two crucial self-reinforcing feedbacks—diffusion (Rogers’ law12) and learning-by-doing cost reductions (Wright’s law13) reinforce and strengthen
each other. Diffusion is also self-reinforcing even without cost reductions, since the increased prevalence ofa new technology implies increased availability to more consumers12,18. Learning-by-doing partly makes theprocess irreversible41. This perspective is consistent with socio-technical transitions theory, for which
substantial qualitative historical evidence exists 17.
The traditional methods used in the climate sciences to identify tipping points require substantial amounts oftime-resolved data to observe critical slowing down, increased volatility and other signatures of tipping
processes42. This is often not possible with available economic data, and few attempts have been made tomeaningfully predict socio-technical tipping points in sector-wide transitions such as a transition to EVs.
Policy contexts and consumer preferences are key drivers conducive of technology transitions4,43,44. Policy
developments in the past decade, notably since the adoption of stringent emissions targets in the EU andthe UK, and pollution regulation in China, may have strengthened relevant positive feedbacks leading to anear term tipping point.
Zero-emissions vehicles (ZEV) mandates that set a target for the percentage of ZEVs in the car fleet to be
sold annually also accelerate the shift to EVs45. Ten states in the US have introduced ZEV mandates, with
the ZEV credit requirement becoming increasingly stringent46.
More recently, many regions/countries have introduced EV mandates alongside increasingly stringent CO2
standards. In the EU, in addition to the existing fleet-wide average CO2 standards, from 2020, a super-credit
system increases the weighting of zero and low emissions vehicles in the calculation of average emissions47.
In China, manufacturers have been subject to a specific annual Corporate Average Fuel Consumption(CAFC) target. From 2019, a dual-credit scheme allows car manufacturers to use surplus New Energy
Vehicle (NEV) credits from EV production to offset CAFC credit deficits48.
As a result of these past transport policies which have induced necessary investments in energy innovation,
based on the empirical data, many markets may now find themselves ripe for a rapid EV transition.
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3. Empirical evidence and fleet simulations
Figure 1 shows the ongoing rapid rise in EV and PHEV sales concurrent with rapid declines in prices 28,30,32–
34,49–55. In our combined Rogers12 and Wright13 model of diffusion of innovations (see Methods), diffusion andlearning-by-doing reinforce each other and cause one another to be stronger. This effect appears to havematerialised during the past decade, where EV and PHEV sales have grown at a consistent global averageof 40% per annum, while costs have declined by a consistent average of 17% per annum. The market
dominance of China, the US, and Europe is visible. EV and PHEV sales are only beginning to take off in
India.
Given the inertia involved in technological change, whether due to strengthening industry supply chains
and/or increasing consumer knowledge and confidence in the technology, this rapid rise is unlikely to slow
down or reverse in the near term unless policy frameworks regress drastically in all three major markets. The
current trend is projected to continue (Extended Data Fig. 1), and the cost of batteries will continue to fall
(Extended Data Fig. 2).

Figure 1 | Diffusion history for electric vehicles. Rapid rise of EVs and PHEVs in the major car markets
and worldwide (right axis) and concurrent rapid battery cost reductions (left axis).

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Figure 2 shows the most likely direction of evolution of mid-range EV ownership costs and prices, according
to the combined Rogers-Wright law, against ICEVs in all four major car markets until the policy horizon of2050, given observed trends, existing uncertainties and assuming that current policy frameworks aremaintained (see Extended Data Fig. 3-4 for other market segments). These projections were simulated using
FTT:Transport on the basis of observed cost and diffusion data for the past 12 years (see Methods).
Ownership costs include discounted lifetime fuel and maintenance costs with a consumer discount rate of
20%56,57. A median learning rate of 20% for battery cost reductions is used as a central estimate 27–35, alongwith ±10% variations to represent a 95% confidence range.

Figure 2 | Projected cost declines for EVs and PHEVs against ICEVs. Trajectory of total ownership costs(dashed lines) and prices (solid lines) of mid-range EVs/PHEVs/ICEVs (median and 95% confidence range
learning rates).
For a mid-range car, ownership costs of EVs achieve parity with ICEVs between 2023 and 2025 in China,
India and Europe, and by 2030 in the US. However, PHEVs never achieve cost parity with ICEVs, and areabsent in India. Note that since approximately 50% of undiscounted lifetime ownership costs for ICEVs stemfrom fuel use and maintenance costs, while electricity accounts only for around 20%-30% of costs for EVs,
ownership cost parity occurs earlier than vehicle price parity in all markets, with the exact timings depending7

on electricity costs and consumer discount rates. Uncertainty ranges mainly stem from the fluctuating priceof fuel and the rate of battery cost reductions.
Figure 3 shows a comprehensive dataset that covers 2452 models in the four leading vehicle markets,
displayed as the frequency distribution of sale prices. Car sale prices are typically lognormally distributed,
following the income distribution19. Here, we observe the evolution of those distributions through historicaltime, from 2016 to 2021. The trajectory of conventional vehicle sales clearly shows a dip starting in 2019,going into a deep trough for 2020-2021, with little sign of recovery in Europe, China and the US yet visible.

Figure 3 | Evolution of vehicles markets and prices. Price distribution time series for conventional
petrol/diesel vehicles, EVs and PHEVs in Europe, the US, China and India.
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Concurrently, we observe rapidly rising sales and an increase in variety for EVs and PHEVs. EVs far
outpaced PHEVs in their growth, with the latter stagnating in 2019-2021. Crucially, EV sales are entirely
unaffected by the COVID-19 pandemic. Note that overall vehicle sales have dipped in total as EV and PHEV
sales have not compensated for conventional vehicle sales losses. Intriguingly, the peak and dip inconventional vehicle sales started before the pandemic in some regions, and thus cannot be entirely
attributed to the pandemic.
Figure 4 shows total sales as well as the total variety of models in the four markets for various technologies,
including announcements made by manufacturers for the near-term future up to 2026. The variety of ICEVs
remains constant over the historical period, but saturates, peaks or begins to decline in the projected period
according to manufacturer announcements. Concurrently, the variety of low-carbon alternatives rises rapidly
starting in 2018 and continues moderately in the near term.
The role of model variety is important in a technological transition, as it must increase over time whereby
firms attempt to generate increasing sales revenue by operating in increasing numbers of market segments
given consumer heterogeneity (Extended Data Fig. 5). In markets where very few EV/PHEV models areavailable, as in India and to some degree in the US, only a few consumers can find a low-carbon equivalentof the ICEVs. Consumer heterogeneity depends on income, family size, gender, distance driven, desiredfeatures, visual influence and social belonging18
To convince consumers to switch to EVs in markets where their variety is low, the subsidies that could breakeven on a cost basis must bridge potentially wide gaps between the prices of some conventional vehicle
market segments and those of scarce zero-carbon alternatives. However, where variety is as large for EVs
as for ICEVs, break-even subsidies can be relatively low, as they are only required to bridge the average
price difference between EVs and their corresponding conventional counterparts, a difference that is
becoming small in many regions (Extended Data Fig. 6-8). It is therefore imperative that the variety of EVs
continues to increase in all markets to ensure a successful and cost-effective transition. Meanwhile, declining
variety in ICEVs is a tell-tale signal that manufacturers are preparing for the transition.
EV subsidies may not be sufficient and do not ensure success in achieving transitions away from ICEVs,
according to our simulations (Extended Data Fig. 9). Adopting subsidies to break cost parity with ICEVs does
not ensure that bans on fossil fuel vehicles are achieved by their implementation date (notably 2035 in theEU) if manufacturers are not ready to supply the required amounts of EVs in time. Taxes on ICEVs or on fuelmay similarly not achieve sufficient progress. These policies gain effectiveness as the deployment of EVs
progresses. It is therefore highly likely that further policy action is required.

9Figure 4 | Car market evolution in Europe, the US, China and India. A. Evolution of sales of petrol/diesel,
electric vehicles (EV), plug-in hybrid electric vehicles (PHEV) and hybrid-electric vehicles (HEV). B. Numbersof different models available in markets for these four classes of vehicles.
Figure 5 shows simulated scenarios of fleet compositions in the four markets assuming the adoption ofvarious layers of policies, going towards effective frameworks able to achieve bans (stated or hypothetical)
on ICEVs by 2035. Column A shows the current technological trajectory until 2050, in which substantial but
insufficient numbers of EVs come to diffuse in all fleets. Taking advantage of the fact that policy instruments
can synergise58, we sequentially add financial, regulatory and mandate policies in panels B-C-D (see10Methods for detailed policy frameworks; see Extended Data Fig. 9 for the effects of individual policies).
Adding fuel and vehicle taxes for ICEVs and subsidies for EVs higher than breakeven values (column B) to
existing frameworks incentivises faster evolution. Adding to this fuel economy regulations (column C) help
reduce emissions faster in the near term but do not ensure zero emissions by 2050. Adding EV mandates(Column D), which guide manufacturers towards supplying specific numbers of EVs going gradually towards
complete ICEV bans, ensures, in tandem with the other policies such as a biofuel mandate, that zero
emissions at the tailpipe can be achieved by 2050.

Figure 5 | Simulations of comprehensive EV policy scenarios for all major car markets. Current
trajectory (A) indicates where markets are headed without additional policies. (B) The addition of more
stringent road and vehicle taxes and EV subsidies have limited impacts. (C) Fuel economy regulations
accelerate conventional vehicle emissions reductions but have limited impacts on the diffusion of EVs. (D)Adding EV mandates magnifies substantially the effect of the other policies as it expands their effect acrossvehicle users.

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EV transitions can be accelerated if costs are brought down by accelerating the scale of deployment. Notably,
the adoption of EVs in developing countries, where the capacity to act with the public policy may be limited,
can be induced by an earlier transition in the leading vehicle markets. In this mechanism, mass deployment
in leading markets brings costs down below cost parity in outside markets, eventually inducing sales incountries without policy action.
Figure 6 shows the cost differences between EVs and ICEVs in different scenarios of international
cooperation by the leading markets (sensitivities shown in Extended Data Fig. 6-8). Assuming that no regions
implement bans on ICEVs induces the slowest rate of EV cost reductions, reaching parity for mid-range carsin around 2030 in Europe, UK and US, and 2025 in China and India. The adoption of policy frameworks that
achieve ICEV bans by 2035 brings forward the year at which cost parity is achieved in all countries. Parity
can be achieved as soon as 2025-2026 for Europe and the US, and around 2023 for China and India. Thistiming difference could be crucial for achieving climate targets. This has, however, diminishing returns, where
early policy success in additional countries contributes less to accelerating cost reductions, once the three
key leading markets and associated regulatory agencies in China, the US and Europe have aligned effective
low-carbon policies. This suggests that policy action in these three markets could be determinant in inducing
endogenous transitions outside of these markets for the rest of the world.

Figure 6 | International cooperation brings forward cost parity. Analysis of cost parity between ICEVs
and EVs for different scenarios of international cooperation to bring EV costs down. The more countries join
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in, the sooner the cost difference between EVs and ICEVs reaches zero. The impact of adding Rest of theWorld (RoW) is only visible for Europe and the US, where cost parity is reached later. The impact of adding
India is not shown as induced differences are small, the market remaining small relative to others shown.

4. Discussion
Our data suggest that a tipping point towards EVs and away from ICEVs in leading vehicle markets could
arise within the next decade. EV sales are rising exponentially while those of ICEVs are declining as
manufacturers worldwide begin to discontinue many ICEV models while marketing increasing numbers of EV
models. Rising variety in EVs concurrent with declining variety in ICEVs is a key indicator of tipping behaviour.
This suggests the rising commitment of manufacturers towards electric mobility.
Importantly, the onset of reorganisation and retooling of production lines is costly and involves profit
expectations over at least a decade, and therefore can be seen as irreversible within the climate policy
timescale. Meanwhile, the EV market and the associated charging infrastructure will grow and coevolve 59
There may be no way back towards ICEVs once the tipping point is activated. The pace of the transition to
be expected is highly uncertain, however, since the magnitude of social feedbacks remains largely unknown.
A successful transition to EVs in leading vehicle markets is highly likely to eventually spill out into developing
countries. Developing country markets could enable further scale expansion and drive costs down evenfurther back in the leading markets. This could happen once cost parity between EVs and ICEVs is achievedin leading markets, after which the costs of mobility begin to decline below ICEVs worldwide. This mechanism
can be critical to achieving a zero-carbon mobility transition in countries where agency on vehicle sales andmanufacturers is limited.
A transition to EVs is also likely to lead to a drastic transformation of economies at the macroeconomic level.
It may substantially reduce the reliance of most countries on foreign oil supplies, and thereby reduce tradeimbalances. For example, in India, a transition to EVs could save USD200bn/y on oil imports by around 2030
and USD300bn/y by 204060, thereby reinforcing the national financial system, currency and internationalpurchasing power. We stress that reductions in oil imports that necessarily come with EVs more thancompensate for the trade balance effects of importing EVs. A double dividend arises where they are madedomestically.
Given that an irreversible EV tipping point appears to be close in leading markets, policymakers in all
countries may be better off rapidly getting on board. This could accelerate the transition to the pace needed
to decarbonise in time to achieve transport emissions reductions consistent with the Paris Agreement, while
drastically reducing exposure to oil market volatility and dependence on foreign supplies60. International
cooperation and coordination on investment and policy in the leading car markets could substantially bring
down mobility costs, benefiting all, as costly and complex oil extraction is abandoned and replaced byinexpensive renewables to power increasingly simple and flexible electric mobility devices. To achieve this,policymakers must facilitate the development of increasing varieties and sales volumes of EVs to accelerate13cost reductions. This includes developing conducive trade policy, investing in infrastructure, building domestic
comparative advantage in EV-related value chains, adopting bans on ICEVs with strict guiding EV mandates,
and helping create markets in developing countries for low-cost EV technologies.

145. Appendix: Methods
Vehicle population and price distribution time series An extensive original database detailing thetechnological profile of vehicles and their populations was developed starting in 2012, and yearly since 2015,
until 202119. The data for vehicle sales by model and technology (i.e. ICEVs, EVs, PHEVs etc.) were obtained
from MarkLines61, which provides sales data by brand-model since 2004 for more than 60 countries. To verify
that MarkLines comprehensively covers all models available and sold in each country in a way that is
consistent with official national statistics, we checked total MarkLines vehicle sales across models against
the annual sales data from official sources (e.g. China Statistical Yearbook 62, Eurostat63, US Bureau forTransportation Statistics64).
Since vehicle prices can vary significantly between countries for identical brand-models, we visited eachmanufacturer’s website for each brand-model in China, the US, Germany, France, the UK and India, andcollected the suggested retail prices (MSRP), engine sizes (for ICEVs, HEVs and PHEVs) or battery sizes(EVs and PHEVs), energy consumption, weight and driving ranges (EVs only). Given that price variations
across EU countries are generally within a range of 20%, we use prices in Germany as a proxy where data
is not otherwise available. In cases where a brand-model is sold in some EU countries but is not available inGermany, we use price and specifications data from the manufacturers’ websites in France or the UK. We
further verified the brand-model coverage of MarkLines by cross-comparison with the manufacturer website
data thus obtained. To create a comprehensive database to study the diffusion of different vehicle
technologies, this exercise was carried out for all four regions each year from 2016 to 2021.
This database is also used to examine the diversity of vehicle brand names in each regional market. We
determined the weighted mean and standard deviation of prices and the fuel economy for each vehicle
technology and market segment (economic, mid-range and luxury, see Suppl. Note 1), which are used tocalibrate FTT:Transport. The number of brand-models sold each year between 2010 and 2021 is used as atime series for vehicle variety in different car markets over time. This time series was extrapolated until 2026
by adding or removing brand-names on the basis of reports published by MarkLines for each OriginalEquipment Manufacturer (OEM). This documents current vehicle models and their successors, future plansof the OEMs to continue/discontinue existing brand-models and plans to launch new brand-models.
EV/PHEV price projections We construct a relationship between battery prices and EV/PHEV prices
according to manufacturing costs. Electric vehicle manufacturing costs are estimated based on MSRP and
the cost-to-price markup factor. Vehicle prices are distinguished from vehicle manufacturing costs due toautomaker profits and dealer markups65. The markup factors vary across vehicle segments, where markup
factors for small vehicles are lower than larger ones. Subject to uncertainty, the current markup factors for
EVs are lower than for ICEVs, around 10% for compact cars, and 20% for sport utility vehicles 65,66. However,markups are subject to uncertainty since they may constitute both strategic and economic choices for vehicle
manufacturers27. Some manufacturers may also be cross subsiding their EVs using profit margins from ICEVs
to gain market share27,30. We assume that to stay competitive, vehicle manufacturers do not increase EV
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markups until EVs achieve price parity with ICEVs. Uncertainty over this is discussed in Suppl. Note 2 andSuppl. Tables 4-11.
Manufacturing costs for EVs depend on four key factors: battery price, platform choice, driving range andthe vehicle’s energy use efficiency (where more efficient use of electricity allows for smaller and cheaper
batteries for a given driving range)27. Among these key factors, the fall in the cost of batteries typicallyaccounts for 75% of current falls in EV manufacturing costs. Battery costs are estimated dynamically with alearning curve based on Wright’s law (see below), and changes in the cost of batteries are included in vehicle
manufacturing costs to determine prices on the basis of markups.
Historically, affordable EVs have had short ranges, while long-range EVs have generally been more
expensive due to larger battery sizes. Over time, as the costs of batteries fall, it has been a common trend
for many car manufacturers to increase the battery size rather than cut EV prices, which thus increased theavailability and variety of long-range EV. In our analysis, EVs were segmented on the basis of battery sizes,
since the latter is proportional to power and range and can thus follow our ICEV engine size classification(see Suppl. Note 1 and Suppl. Figure 2 for vehicle segmentation). We consider that driving range increases
as consumers switch market segments in our model.
In the current trajectory scenario, we assume that vehicles are developed and manufactured using platforms
modified from existing ICEVs. In the next ten years, it is possible that many EVs will be designed andproduced based on new specially designed platforms27,61. Over time, production costs and indirect costs,
including research and development as well as amortised costs for investment in expanding EV production
capacity are all likely to decline. There are fundamental uncertainties regarding platform choices in the longrun. In our analysis, these uncertainties are treated as part of the price sensitivity analysis (see Suppl. Tables
4-11).
Total costs of ownership The total cost of ownership (TCO) is the present value of all relevant costs inowning and using a car67,68. Here we express this as:
𝑁

𝑇𝐶𝑂𝑖 = 𝑃𝑟𝑖 + (𝑉𝑇𝑖 − 𝑆𝑖 ) + ∑𝑛=1(𝐹𝐶𝑖 + 𝑀𝐶𝑖 ) × 𝑇𝐷 + 𝑅𝑇𝑖(1 + 𝑟)𝑛(1)

Vehicles prices (𝑃𝑟𝑖 ) for vehicle category i are those discussed above, while (𝑉𝑇𝑖 − 𝑆𝑖 ) is the net tax/subsidy
applied to that vehicle category, and 𝑅𝑇𝑖 is a yearly road tax.
Fuel Costs (𝐹𝐶𝑖 ) Depend on the vehicle’s energy consumption per kilometre, obtained from official
manufacturers’ websites in 2020, where fuel/electricity prices are obtained from various data sources listed
in the Suppl. Table 1. In the reference scenario, we assume that future gasoline prices follow the IEA baseline
scenario69. The retail gasoline price is subject to uncertainty. We analyzed the effects of low and high oil
prices on EV adoption and cost parity for ICEVs (see Figure 2 and assumptions in Suppl. Dataset). Electricity
cost for EV depends on the price of electricity at different charging sites. In the reference scenario, there are16three charging scenarios, representing 100%, 75%, 50% of charging at home at a lower cost, and 0%, 25%,50% via public charging at a higher cost.
Maintenance costs (𝑀𝐶𝑖 ) are uncertain and here assumed to be $0.04, $0.05, $0.06 per kilometer for all three
ICEV market segments (economic, mid-range, luxury)65. Conventional EVs have fewer moving parts thanICEs, and hence maintenance and repair costs are typically lower compared to ICEVs. We assumed thatEVs cost 50%(±20%) less to maintain32,65. PHEV per kilometre maintenance cost is assumed to be the equal
to the cost of ICEV for each vehicle segment 65.
The baseline analysis assumes that each vehicle’s yearly distance driven (TD) is 15,000 km and that
consumers use a 20% discount rate (r), on average. In the sensitivity analysis, we examine how the TCO
varies with different discount rates and annual mileage (see Supplementary Note 2 and Suppl. Tables 4-11).
Note that the lifetimes of vehicle ownership can vary from 3 years70 to up to 15 years71 typically following asmooth survival function with life expectancy of around 11 years. Due to the uncertainty in the EV resale
value, a 12-year ownership period, is considered in this research72. Given a high baseline discount rate andcar depreciation73, we consider the resale value of a car after 12 years negligible.
EV and battery pricing Battery price is considered the main barrier to widespread EV adoption. Falling
prices for lithium-ion batteries are the biggest driver supporting rapid EV penetration27. Existing studies havemodelled battery production cost reduction using conventional learning curves28,74–77. Also known as Wright’s
Law13, the concept of the learning curved is based on the empirically observed phenomenon that the unit
cost of a technology declines by a constant percentage for each doubling of cumulative production volume(e.g. cumulative installed capacity), as described by the following equation:
𝑃 −𝑏𝑖
𝐶𝑡 (𝑃) = 𝐶0 ( )𝑃0(2)Where 𝑃 is the total global cumulative production of the technology (i.e. the total kWh capacity of cells
produced), 𝐶 is the cost per unit (USD/kWh, 𝐶0 and 𝑃0 are the initial cost and cumulative production at thestart date of scenarios, while b is the learning exponent. The latter is related to the cost reduction that resultsfrom every doubling of production, what is known as the learning rate (𝐿𝑅) (within a range of 10%-30%),
through
𝐿𝑅 = 1 − 2−𝑏(3)

Importantly, this model assumes that knowledge diffuses globally (vehicle manufacturers operate globally
and/or source batteries from a common set of competitive technology suppliers), and therefore that
production in all countries contributes to declining costs in all countries. As a result, costs in all countries are
influenced by sales in other countries. This is supported by the empirical evidence in a wide range of cases13.
Several studies have developed multi-factor learning functions that consider factors such as economies ofscale, learning by searching and material costs78,79. However, in practice, multi-factor and two-staged
17

experience curves are difficult to construct due to data limitations. For example, EV battery learning throughresearch or spillover effects from other industries are difficult to reliably quantify80. Hence, in this article, we
adopt the single factor learning curve, which is the most widely used approach for battery cost
forecasts33,55,76,77,81.
In the current trajectory scenario, we derived the projected nickel and cobalt prices from existing studies82,83,
and the impact of material price changes on the battery pack price is based on an analysis by Bloomberg
NEF27 (see Suppl. Dataset for details). We consider the surging rare metal prices a result of recent supply
disruptions and increased global demand for batteries84. The battery prices may pick up as a result of therare metals price surge in the next few years84. We analysed the effect of rare metal price shock on the cost
parity in Suppl. Note 8. As demand for battery materials increases, more investment flows into expanding the
global capacity for mineral extraction27. Overall, the impact of material prices on the price of a battery packand the TCO is moderately low relative to other factors27,85 (see Suppl. Tables 4-11). Furthermore, as batteryenergy densities improve over time, reliance on critical materials decreases. The impact of improvement inbattery energy density and higher critical material costs is examined in the sensitivity analysis(Supplementary Note 8 and Suppl. Tables 4-11).
Estimated current EV lithium battery costs vary widely and are subject to great uncertainties 27–35. Figure 1
and Extended Figure 2 illustrates the historical and projected battery pack costs reported by different
automakers and studies32. For our current trajectory scenario, an average battery cost of $150/kWh in 2020
at the pack level is assumed.
Analyses of lithium-ion cells have estimated a wide range of learning rates, spanning 6% to 30% 27–35. In thereference scenario, we use a 20% learning rate, which matches the median estimates of the observed battery
learning rates between 1991 and 2020. A sensitivity analysis was carried out using the reported ranges foundin underlying data (see Supp. Tables 4-11).
Overview of the vehicle fleet projection modelling framework. The Future Technology Transformations
for transport (FTT:Transport) model is a submodule of the integrated assessment model named E3ME-FTTGENIE, a simulation framework that covers the economy, technology and the climate. The FTT family ofmodels consists of FTT:Power86, FTT:Transport25, FTT:Heat87, and FTT:Steel88 and is a bottom-up
representation of the technological change that reproduces past and projects future diffusion patterns forindividual technologies calibrated on observed trends. The FTT framework models technological diffusion
using a set of coupled finite differences equations of the Lotka--Volterra family, which represent gradualtechnological substitution processes86. Under the FTT framework, consumers are proportionally more likely
to choose a technology that has a higher market share as a result of its availability, visibility, social influenceand network effects, all of which we represent combined simply as adoptions proportional to the current
market shares. Such bandwagon effects are well known and substantially influence the profiles of diffusionof vehicle models3,18. Including this network effect in an otherwise standard discrete choice model is18mathematically the same as the replicator dynamics equation of evolutionary systems, and generates the
widely observed S-shaped technology diffusion profile (Rogers’ law)12,89 (See Suppl. Note 3).
In the FTT-Transport model, we assume revealed preferences, where the observed cost distribution forrecent vehicle sales corresponds to the heterogeneity of consumer preferences and choices19,25. Following
evolving choices and preferences, a flow of market shares generally arises from any arbitrary technology (𝑖)category towards every other category (𝑗), denoted as follows:∆𝑆𝑖→𝑗 ∝𝑆𝑖 𝑆𝑗
𝐹 (∆𝐶𝑖𝑗 )∆𝑡
𝜏 𝑖𝑗

(4)

A reverse flow also exists from technology to technology as shown here:∆𝑆𝑗→𝑖 ∝𝑆𝑗 𝑆𝑖
𝐹 (∆𝐶𝑗𝑖 )∆𝑡
𝜏 𝑗𝑖

(5)

∆𝑆𝑖→𝑗 denotes the flow of shares from vehicle category i to category j, 𝜏 is the turnover rate for vehicles (thefrequency at which new vehicle buying choices are made). 𝐹𝑖𝑗 represents the fraction of agents that prefer
technology i over j based on the difference in mean generalised (perceived) cost of the two vehicle options
relative to agent diversity ∆𝐶𝑖𝑗 (see Suppl. Note 4). The latter is derived from observed cost distribution. The
net flow of shares between vehicle categories generates the composition of sales in the next time period.
These are used to determine the composition of vehicle fleets over time.
Total vehicle ownership projections. To project battery costs, a scenario for EV and PHEV sales isrequired, which itself requires projections of total vehicle ownership across markets. Vehicle ownershipmodels are used to forecast transport demand, energy consumption, and emission levels. Among thedifferent model types, one of the most well-known approaches is an econometric estimation of an incomecar stock model based on a logistic function90. The Gompertz curve is an S-shaped growth curve that relates
per capita vehicle ownership to GDP per capita. While vehicle scrappage is not explicitly included, it has
been tested empirically to represent the growth trend of vehicle stock90. We examine trends in the growth ofvehicle fleet numbers for a large sample of countries and employ the Gompertz function to estimate therelationship between the number of vehicles and per capita income (see Suppl. Note 5). The fleet numbers
for individual technologies are calculated based on the projected shares of the technologies and the total
fleet number.
Policy scenario analysis Current trajectory The baseline scenario is used as a benchmark against which toassess policy scenarios with added policy action. Existing policies are assumed to remain in place implicitly
through our reliance on economic, energy, price and technology diffusion (behavioural) data.

19

Current Trajectory with taxes and subsidies Vehicle registration, purchase taxes and EV subsidies are added
to the policies implicitly assumed in the current trajectory. The costs of taxes and subsidies (negative cost)
are added to Equation 1. Here, we assume that all taxes on ICEVs are becoming increasingly stringent: 50%
higher than the current levels in each country. The level of EV subsidy/tax credit will be 50% higher than thecurrent level, but this will be reduced gradually after 2030, and phased out after 2035. We assumed that the
EV subsidy scheme will not be revived after it has been removed (e.g., in China).
Current Trajectory with taxes and subsidies and regulations. Besides financial incentives, fuel economy
standards require automakers to design more efficient vehicles or to shift sales towards more efficientmodels. Following the proposals by the EU and US, we assumed that ICEVs with below-average fleet-wide
CO2 emissions in 2020 will be phased out in 2025 in all regions for consistency, replaced by more advanced
models that are on average 20% more efficient than that of the current ICEVs (see the Suppl. Dataset for theassumptions).
Fuel economy regulations are modelled by influencing the flow of market shares in each technology category.
We assume that there are no new market shares gained in the categories being phased out.
Current Trajectory with taxes and subsidies, regulations and EV mandates. Since the EV mandates areabsent in most states in the US, EU, India, we assumed that these would be introduced and that theirstringency would lead gradually towards a complete ban on ICEVs in 2035. We assume that market shares
flow from ICEVs to EVs by setting exogenous market share additions at specific points in time to be consistent
with the mandate, although EV market shares can exceed the mandate value.
Our approach is not exactly the same as that of the real-world EV mandates, for which the government either
sets an EV production quota (e.g. the China New Energy Vehicle (NEV) mandate and the California ZEV
mandate program) or assigns extra credits for producing EVs (e.g. super credits for zero- and low- emissions
vehicles (ZLEV) in the EU). Our approach mimics most closely the impact of EV mandates in which thegovernment imposes increases in the share of EVs in total sales.
Regions Europe includes EU27, Norway, Iceland, Switzerland and the United Kingdom. The rest of the world
includes Japan, Korea, Australia, New Zealand, Canada, Brazil, Chile, Mexico, Argentina, South Africa,
Indonesia, Thailand, Malaysia, Cambodia. Other includes countries under ‘the rest of the world’ and India.

20

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Extended Data Fig. 1 | Projected EV stock and stock share in 2025 and 2030. Comparison between the
Current Trajectory Scenario and the Strong Policies Scenario presented in this paper, alongside IEA
projections in the Stated Policies Scenario (STEPS) and Sustainable Development Scenario(SDS)91. The bars are the projected EV stocks and the dots are the projected EV stock shares.

27

Extended Data Fig. 2 | Projected cost of Li-on battery packs in EVs. Three initial battery costs (in 2020) aretaken to reflect the uncertainties in the cost of battery packs reported by existing studies or leadmanufacturers. The black curves are the projected battery costs under the assumption of a 20%
learning rate. The dash curves reflect the projected battery costs under different learning rate
assumptions. Left panel: projected cost of battery packs under the Current Trajectory Scenario, incomparison with the results from the existing studies. Right panel: projected costs of battery packs
under the Strong Policies Scenario.

28

Extended Data Fig. 3 | Projected cost declines for economic EVs and PHEVs against equivalent ICEVs.
Trajectory of total ownership costs (dashed lines) and prices (solid lines) of economic
EVs/PHEVs/ICEVs (median and 95% confidence range learning rates).

29Extended Data Fig. 4 | Projected cost declines for luxury EVs and PHEVs against equivalent ICEVs.
Trajectory of total ownership costs (dashed lines) and prices (solid lines) of luxury
EVs/PHEVs/ICEVs (median and 95% confidence range learning rates).

30

Extended Data Fig. 5 | Relationship between model variety and the breakeven subsidy. The breakeven
subsidy declines as costs decline with rising investment, for (a) constant EV variety, (b) doubled EV
variety and (c) EV variety matching the current variety of petrol/diesel vehicles.

31Extended Data Fig. 6| Break-even subsidies to achieve cost parity with sensitivity analysis for economic cars.
Break-even subsidy analysis for different scenarios of international cooperation to bring EV costs
down. The more countries join, the sooner the break-even subsidy becomes unnecessary. The
empty panels indicate that cost parity was reached in the EU and China.

32

Extended Data Fig. 7| Break-even subsidies to achieve cost parity with sensitivity analysis for mid-range cars.
Break-even subsidy analysis for different scenarios of international cooperation to bring EV costs
down. The more countries join, the sooner the break-even subsidy becomes unnecessary.

33Extended Data Fig. 8 | Break-even subsidies to achieve cost parity with sensitivity analysis for luxury cars.
Break-even subsidy analysis for different scenarios of international cooperation to bring EV costs
down. The more countries join, the sooner the break-even subsidy becomes unnecessary. The
empty panels indicate that there is no luxury segment EV sold in India currently.

34

Extended Data Fig. 9 | Simulations of individual EV policies for all major car markets. Current trajectoryindicates where markets are headed without additional policies. The policy scenarios indicate where
policy incentives are introduced individually.

35

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