sustainability
ArticlePolicies and Predictions for a Low-Carbon Transition
by 2050 in Passenger Vehicles in East Asia: Based on
an Analysis Using the E3ME-FTT Model
Aileen Lam 1,2, *, Soocheol Lee 3 , Jean-François Mercure 2,4,5 , Yongsung Cho 6 , Chun-Hsu Lin 7 ,Hector Pollitt 2,5 , Unnada Chewpreecha 5 and Sophie Billington 5
1
2
3
4
5
6
7

*

Department of Economics, Faculty of Social Sciences, University of Macao, E21, Taipa, Macau, China
Department of Land Economy, University of Cambridge, 19 Silver Street, Cambridge CB31EP, UK;
J.Mercure@science.ru.nl (J.-F.M.); hp@camecon.com (H.P.)
Faculty of Economics, Meijo University, 501 Shiogamaguchi, Tenparku, Nagoya 468-0073, Japan;
soolee011@gmail.com
Department of Environmental Science, Radbound University, 6500 GL Nijmegen, The Netherlands
Department of International Modelling, Cambridge Econometrics, Covent Garden, Cambridge CB12HT, UK;
uc@camecon.com (U.C.); sh@camecon.com (S.B.)
Department of Food and Resource Economics, Korea University, Anamdong, Seoul 02841, Korea;
yscho@korea.ac.kr
Center for Green Economy, Chung-Hua Institution for Economic Research, Taipei 106, Taiwan;
chlin@cier.edu.tw
Correspondence: meimeilam@umac.mo

Received: 27 February 2018; Accepted: 13 May 2018; Published: 17 May 2018


Abstract: In this paper we apply a model of technological diffusion, Future Technology
Transformations in the Transport Sector (FTT: Transport), linked to the E3ME macroeconomic model,
to study possible future technological transitions in personal passenger transport in four East Asian
countries. We assess how targeted policies could impact on these transitions by defining four
scenarios based on policies that aim to reduce emissions from transport. For each country wefind that an integrated approach of tax incentives, subsidies, regulations (fuel economy efficiency),
kick-start programs and biofuel programs yield the most significant emission reductions because,
when combined, they accelerate effectively the diffusion of electric vehicles in the region.
Keywords: CO2 emission reduction; transport sectors; FTT: Transport model; E3ME; East Asian
countries; transportation policies; the diffusion of electric vehicle

1. Introduction
As the Intergovernmental Panel on Climate Change 5th Assessment Report (AR5) highlighted,
the transportation sector will need to play a key role in climate change mitigation, but will certainly
face challenges in doing so [1]. Demand for road transport in East Asian countries, including China,
Japan, Korea and Taiwan, has increased rapidly over the last two decades. Vehicle emissions and oil
demand growth is driven primarily by growth in the vehicle population, especially private passenger
cars. As of 2016, more than 250 million passenger cars were registered in China, Japan, Korea andTaiwan. The number of passenger cars in China increased by a factor of 9 between 2006 and 2016,
compared with a per capita GDP increase of only 4.
The demand for crude oil and petroleum by the road transport sector has increased by nearly
60% in China, Japan, Korea and Taiwan since 2004 (IEA Energy Statistics). Passenger cars were foundto account for over 50% of the increase [2,3] in gasoline demand. The dependence on fossil fuels has
significant implications on energy security, emissions and climate change.
Sustainability 2018, 10, 1612; doi:10.3390/su10051612

www.mdpi.com/journal/sustainability

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The use of existing technologies makes it possible to reduce dependency on fossil fuels,
decarbonize, and reduce emissions in the passenger car sector. The dynamics generated by the
diffusion of new technologies into the market place, and the improvement of existing technologies,
determines technological trajectories that are critical to reducing future emissions from passenger
vehicles [4]. As stated in the IPCC report [1], understanding how low-carbon transport and energy
technologies will evolve (via experience curves and innovation processes) is not well developed, andassessing this gap remains challenging for the transportation sector.
The successful implementation of policies to reduce greenhouse gas (GHG) emissions frompassenger cars is particularly challenging because of the dynamics of technological transition andconsumer behavior. To design efficient policy instruments, knowledge of the mechanisms that facilitate
the diffusion of new car technologies is needed. It is important to determine the relationship between
policy instruments and the rate of adoption for low or zero-emission vehicles.
Previous studies that have assessed the effectiveness of policy incentives in reducing emissions
from passenger vehicles have typically focused on a single country or region in Asia (e.g., [5–8]).
These studies have looked at the effectiveness of taxation schemes (vehicle taxes, carbon taxes) andregulations (fuel economy standards) in shaping the diffusion of energy-efficient vehicles. We argue
that there are two major gaps in existing studies:
(1)

(2)

Most existing studies and Integrated Assessment Models (IAMs) feature only a small number ofpolicy instruments, applied to individual East Asian countries (e.g., [5,9]). It is not possible
for these studies to study and compare the effectiveness of a range of policy instruments
across countries.
The existing studies have either taken one particular representative car model or used a
representative agent to examine the response of agents to a set of policy incentives. In thereal world, consumers are diverse and do not respond to policy incentives collectively.

In this paper we use a model of technological diffusion, the FTT: Transport [10], linked to the
E3ME macroeconomic model, to study possible future technological transitions in personal transport
in East Asia. Our analysis covers China, Japan, Korea and Taiwan and includes a range of policy
incentives. In contrast to other IAMs, the E3ME-FTT model considers consumer heterogeneity in the
car market and enables the simulation of detailed climate policies in private road transportation [10].
We consider four policy scenarios, each with a layer of specific policy incentives for the four East
Asian countries, to find a set of policy incentives that will lead to the diffusion of new energy vehicles(NEVs) and significant emission reductions from private passenger vehicles.
The paper is structured as follows. Section 2 discusses the current policy framework for reducing
emissions in the passenger car sector in China, Japan, Korea and Taiwan. The theoretical framework of
the E3ME-FTT model is discussed in Section 3 and the policy assumptions are summarized in Section 4.
Section 5 provides an overview to the data used for the analysis. Section 6 presents the results
for the scenario analysis and the policy implications for the East Asian countries. Sections 7 and 8
provide overall conclusions and discuss the limitations of the model, along with recommendations for
future work.
2. Policy Context
This section summarizes the main policies that have been implemented in East Asia to limit
emissions from personal transport. First, Table 1 provides an overview of fuel economy standards
around the world. The following sections then discuss each East Asian country individually.

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Table 1. Overview of regulation specifications for passenger cars in major economies.
Country or
RegionTarget Year

Unadjusted Fleet
Target/Measure (L/100km)

Structure

Test Cycle

EU

2015
2021

5.6
4.09

Weight-based average

NEDC

China

2015
2020 (proposed)

6.9
5Weight-class based per vehicle
and corporate average

NEDC

US

2016
2025

6.5
4.19

FP-based corporate average

US combined

Japan

2015
2020

5.95
4.93

Weight-class based corporate
average

JC08

Brazil2017

5.32

Weight-based corporate average

US combined

India

2016
2021

5.6
4.87

Weight-based corporate average

NEDC forlow-powered vehicle

Korea

2015

5.88

Weight-based corporate average

US combined

Mexico2016

5.99

FB-based corporate average

US combined

Source: The International Council on Clean Transportation (ICCT, 2015), global comparison of fuel economy/GHG
standards for passenger cars.

2.1. China
Due to China’s rapid economic growth, demand for private passenger vehicles has grown rapidly.
From 1991 to 2015, total annual vehicle production grew from around 700,000 units to 24 million units.
Annual sales of passenger cars have grown by more than 20 times since 1990. By the end of 2015,
there were more than 116 million cars on the road in China. However, in terms of passenger cars
per 1000 people, China was measured at 154 cars in 2016 (http://www.mep.gov.cn/gkml/hbb/qt/
201706/t20170603_415265.htm), which is still much lower than the USA, so the potential for future
growth remains large.
At the national level, to reduce its dependency on foreign oil and to encourage more fuel-efficient
vehicle technologies, the passenger vehicle market has been subject to fuel economy standards since2004. Fuel economy limits for passenger cars are divided into 16 categories that are based on vehicle
weight. The Phase I and Phase II standards require each individual vehicle model to comply with fuelconsumption regulations before entering the market. The standards are set based on weight classes.
China continues to reduce the fuel consumption limit and Phase IV fuel consumption standards forpassenger vehicles are currently under development. In 2014, the Chinese Ministry of Industry andInformation Technology (MIIT) released a fuel consumption standard for passenger cars. Comparedto the Phase III standard, the new consumption standard would fall to 5 l/100 km, representing an
overall reduction in fuel consumption of 28% between 2015 and 2020. The proposed fuel economy
standard is presented in Table 1.
Electric Vehicles (EVs) offer opportunities to address oil security, local pollution and GHG
emissions simultaneously; EV deployment is taken by the central government as an essential strategy
to tackle local pollution. China launched the EV Subsidy Scheme (EVSS) in 2009, followed by an
update in 2013. Under Phase I EVSS, subsidies for the private purchase of plug-in hybrid electricvehicles (PHEVs) and battery electric vehicle (BEVs) are based on battery capacity, with a subsidy
rate of 3000 RMB/kWh (Hao, 2014). Under Phase II, subsidies for the private purchase to PHEVs
and BEVs are based on the vehicle’s electric range. Vehicles with electric ranges of 250 km or higher,of 150–250 km, and of 80–150 km are qualified for 60,000 RMB (9520 USD), 50,000 RMB (7930 USD),
and 35,000 RMB (5550 USD) in subsidies, respectively (Hao, 2014). Table 2 presents China’s Phase 1
and Phase 2 Electric Vehicles (EV) Subsidy Scheme (EVSS).

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Table 2. China’s Phase 1 and Phase 2 Electric Vehicles (EV) Subsidy Scheme (EVSS).
Criteria\Phase

Phase 1

Phase 2

Target market

PrivatePublic/private

Subsidy duration

2010–20122013–2015

Subsidy scope

Plug-in hybrid electric vehicles (PHEVs)
Battery electric vehicles (BEVs)

Plug-in hybrid electric vehicles (PHEVs)
Battery electric vehicles (BEVs)
Fuel cell electric vehicles (FCEVs)

HEV

-

-

PHEV

3000 RMB/kWh

35,000 RMB (range ≥ 50 km)

BEV3000 RMB/kWh

35,000 RMB (80 ≤ range < 150 km)
50,000 RMB (150 ≤ range < 250 km)
60,000 RMB (range ≥ 250 km)

Phase-out mechanism

Not specified

10% reduction in 2014

Pilot cities

Six cities

28 cities and regions

Subsidy
standard

Environmentally-friendly and energy-efficient vehicles, including plug-in hybrid electric vehicles(PHEVs) and battery electric vehicles (BEVs), are exempt from vehicle excise duty and annual
registration tax. Before 2010, car excise duty rates were based on engine size; with small engine
cars enjoying a lower tax rate. After 2010, the excise duty was 10% before value-added-tax (17%)
price regardless of car engine sizes. Registration tax was paid annually based on engine size and as
established by individual provinces or cities.
In 2009, the Chinese government initiated the Ten Cities, Thousand Vehicles Program to stimulate
electric vehicle development through large-scale pilots in ten cities. Initially, the program targeted thedeployment of electric vehicles in government fleets. The program has since expanded to 25 cities
and includes consumer incentives in six cities: Beijing, Shanghai, Hangzhou, Hefei, Changchun and
Shenzhen [11]. Under this program, local authorities must increase the number of EVs on the road byeither purchasing EVs themselves or introducing a rebate/subsidy for EV purchases. The programaims to kick-start demand for EVs.
The sales of EVs and PHEVs increased from 8000 units in 2011 to 330,000 units in 2015. Table 3
shows the change in EV and PHEV shares in China over this period. China had the largest share of
global EV and PHEV sales in the world (33%) in 2015. China is targeting a domestic share of 20% NEV
sales (2 million) by 2025 (http://www.reuters.com/article/us-china-autos-electric-idUSKBN17R086).
The Chinese government has also announced that companies which sell cars in China will have aduty to sell a certain share of NEVs. In 2017, China’s MIIT proposed a production quota under which
automakers will have to produce NEVs to earn credits and avoid penalties. According to MIIT, in 2019
automakers that sell 30,000 cars or more annually must earn points equivalent to a 10% share of NEVs,
rising to 12% in 2020.
Table 3. Market shares of EVs and plug-in hybrid electric vehicles (PHEVs) in China between 2011 and 2015.
Type\Year

2011

2012

2013

2014

2015

EV

0.056%

0.074%

0.082%

0.227%

1.192%

PHEV

0.006%

0.006%

0.017%

0.152%

0.374%

Total

0.056%

0.081%

0.098%

0.379%

1.57%

Source: China Association of Automobile Manufacturing (CAAM).

2.2. Japan
Japan is the third largest auto manufacturing market, and Japanese automakers account for the
majority of global hybrid cars and electric cars sales (http://www.theicct.org/blogs/staff/hybridsbreak-through-japan-auto-market). Toyota is the largest Japanese car manufacturer, holding aroundSustainability 2018, 10, 1612

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43% market share; Nissan and Honda are the second and third largest, with 17% and 15% shares
respectively. More than 90% of the cars sold in Japan are Japanese [12]. Under the Paris Agreement,
the Japanese government pledged to reduce its national GHG emissions by 26% from 2013 levels by2030. Japan has one of the most energy-efficient economies in the world and its transportation sector isalso among the most efficient.
Table 4 shows the market shares of next-generation vehicles in Japan. Sales of next-generation
passenger cars have expanded to more than 20% share of Japan’s new passenger car market because of
the Japanese government’s tax incentives and purchasing subsidies programs on them. The share of
NEVs in Japan’s fleet remains relatively small but is growing quickly.
Table 4. Market shares of EVs, PHEVs, and Fuel Cell Vehicles (FCVs) in Japan between 2011 and 2015.
Type\Year

2011

2012

2013

2014

2015

EV

0.635%

0.847%

1.201%

1.504%

1.908%

PHEV

0.118%

0.378%

0.662%

0.936%

1.354%

FCV<0.001%<0.001%<0.001%0.003%

0.015%

Total

0.753%

1.225%

1.862%

2.444%

3.227%

Source: JAMA (2017), Automobile Statistics Japan, Japan Automobile Manufacturers’ Association.

Japan was one of the first countries to engage in research and policies for energy-efficient products.
In 1998, Japan initiated the Top Runner Approach to encourage energy efficiency in end-use products.
The scope of the program has since been reviewed every few years; by 2012, 23 products were included.
The program has set mandatory efficiency standards or target values for automobiles that are based on
the most efficient products. As part of the Energy Conservation Law, the program identified the mostfuel-efficient automobile in each weight class and designated it as the ‘top runner’. The program set
mandatory efficiency standards or target values for automobiles based on the most efficient standard
products. All vehicles were required to exceed the new target values for their weight class within
3–10 years.
As a result of the Japanese top runner program, Japanese fuel economy for the new vehicles has
improved significantly over the past 20 years. Overall, fuel economy has improved by more than 80%
since 1995, and it has averaged a 6% annual improvement over the past five years [13].
There are nine different taxes for owning cars in Japan, including: acquisition tax, consumption
tax, tonnage tax, automobile tax, mini-vehicle tax, gasoline tax, diesel tax, Liquefied Petroleum Gas(LPG) tax, and in use consumption tax [14]. Tax breaks are available for three automobile taxes:
acquisition, tonnage, and ownership. Tax breaks are determined by the levels of compliance withthe 2015 Japan Fuel Economy Standards (FES). Zero emissions vehicles are exempt from both theacquisition tax and the tonnage tax. Cars that are compliant with the 2015 FES enjoy up to 80%
reductions in acquisition tax and 75% reductions in automobile tonnage tax [14].
2.3. Korea
The transportation sector accounts for about 20% of greenhouse gas emissions in South Korea
and is the country’s second largest emitter, trailing only the industrial sector. Within the transportation
sector, road transport dominates with an emissions share of over 80%. Thus, reducing transportation’s
impact on the environment should require transforming the current automobile-based transportsystem into a more energy-efficient, low-carbon and eco-friendly one. Sustainable development intransportation has also been a major concern for Korea. Both vehicle ownership and transport demand
have increased substantially during the past two decades. As a result, energy consumption has
increased rapidly, and vehicles have become the major source of urban air and noise pollution in many
Korean cities. Current growth rates shows no sign of subsiding.

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One characteristic of automobile markets is that the demand for diesel cars has been increasingin the car market. Diesel vehicles, especially imported diesel cars, have dominated in the green car(environmentally-friendly car) market. The share of diesel in imported vehicles increased from 16.4%
in 2007 to 68.2% in 2014. SUVs and MPVs led the sales of domestically produced diesel. Sedans havebeen popular, but SUV demand has been increasing in the imported diesel market.
Meanwhile, sales of hybrid vehicles have been increasing continuously. Hybrid vehicles arecompeting with diesel vehicles in the domestic market. However, imported hybrid car sales have beensluggish. Regarding EVs, prices have been falling and government subsidies have been increasing.
For example, Kia automobile company cut its Ray EV price from 45 million Won (41,850 USD)
to 35 million Won (32,550 USD). Local governments have also provided subsidies to EV buyers.
In particular, Jeju special self-governing province has subsidized 8 million Won (7440 USD). The
actual purchasing price was 19.5 million Won (18,135 USD) before tax. However, the lack of central
government purchasing subsidy has hindered sales. For EVs to achieve a larger market share, there isalso a requirement to expand charging infrastructure throughout Korea.
The key challenges for a low-carbon transition in the transportation sector in Korea may besummarized as follows:
(1)
(2)

(3)

Substituting fossil fuel cars with EVs requires a reduction in the cost of batteries (which depend
on international technology actions) and building a nationwide charging infrastructure.
Green Car Developments: Current heavy fossil fuel dependence of transport should be reduced
by diversifying energy sources. NEVs offer a promising alternative to conventional vehicles inshort distance trips in urban areas. Wireless electricity technology could facilitate the introductionof electric vehicles by solving their current battery limitations. Fuel cell technology could be along-term option in future alternative fuel vehicle developments. Legal support and economic
incentives should be provided for the development of these types of green vehicles.
Non-Motorized Transportation: Non-motorized forms of transport provide zero carbon emissions.
Bicycles are used extensively in many European and Asian cities, but their modal share in
Korean cities is minimal due to limited infrastructure and low public reception. Bicycles
should be promoted for short distance commuting by providing adequate infrastructure and byincreasing safety.

2.4. Taiwan
Despite not being a member of the United Nations, Taiwan proposed its goal of CO2 emission
reduction as its Intended Nationally Determined Contribution (INDC) after the U.N. Framework
Convention on Climate Change (UNFCCC) Conference of the Parties (COP21) in Paris in 2015. Taiwan
pledged a 50% reduction in emissions compared to the level of 2005 by 2050. The transportation sector
is a major contributor to greenhouse gas emissions in Taiwan (about 15% of Taiwan’s total emissions)and it is also a major source of local air pollution, such as PM2.5 .
In Taiwan, all vehicles put into the market must comply with the standards of energy consumption,
which are categorized by vehicle engine size. In addition, all vehicles must declare their ranking in theenergy labelling system: Class 1 to Class 5, as the reference of energy efficiencies for the general public
and potential customers.
To encourage the purchase of green vehicles, there are several incentive programs offered bydifferent governmental agencies. The financial incentives include the exemption of excise tax on allelectric vehicles until the end of 2021 and the exemption of registration tax on all electric vehicles until
the end of 2018. For hybrid cars, the excise tax was deducted by 50% from February 2009 to December
2014. There are also direct subsidies offered for purchasing electric motorcycles, electric-assisted
bicycles and electric bicycles. The subsidies range from 1000 NTD to 10,000 NTD (30 USD to 330 USD).
In addition to the direct incentives to reduce greenhouse gas emissions from vehicles, the excise
tax is deducted. In addition, 50,000 NTD, approximately equivalent to 1700 USD, is provided for eachnew passenger car that leads to the retirement of an older car owned by the same family, regardlessSustainability 2018, 10, 1612

7 of 32of the fuel type of the new vehicle. For motorcycles, the deduction of the excise tax is 4000 NTD, or
130 USD in this program. Additional subsidies, 3000 NTD to 7000 NTD (100 USD to 230 USD), areprovided for new motorcycles that are purchased to replace old motorcycles that are equipped with
two-stroke engines.
For most cases, the incentives have been extended beyond their scheduled deadlines, except forthe case of hybrid cars that was terminated in 2014. The termination of the 50% excise tax reduction
was a key factor in the drop in sales of hybrid cars (which are mostly imported), from 17,788 in 2014 to
8341 in 2015.
3. Methodology
3.1. Theoretical Background
The Future Technology Transformation (FTT) model is a framework that models technologicaldiffusion dynamically, based on market price competition and technological competition. Prior to thedevelopment of the Transport model, FTT was developed to assess decision making by investors in
power generation technology [15]. More recently, an FTT model of household heating technologies
was developed and efforts to build FTT models of the industrial sector are ongoing. As a member of
the FTT framework, the FTT: Transport model aims to model technology diffusion dynamically in thetransport sector, based on a decision-making module that represents the choices of a diverse group ofagents that face restricted information and access to technology for consumers.
Instead of taking a representative agent approach, the FTT: Transport model assumes the presenceof a diverse market with heterogeneous agents. This is done using a probabilistic treatment of consumer
decision making using a distribution of cost values. We assume that the cost distribution corresponds
to the heterogeneity of consumer choices as a result of revealed preferences. The cost distributions
are related to whether the consumers are early adopters, early majority, late majority or laggards,
which drives the adoption and diffusion of technology as suggested by [15]. Agent heterogeneity andconsumer choice are represented by introducing cost distribution over agent perspective with thediscrete choice theory.
As a result of increasing returns to adoption [16], a technology can gain increased market share as
more consumers adopt and use it for the following reasons. Firstly, people make choices according towhat is fashionable or in vogue. For example, people like to behave in a ‘socially desirable way’ andmay purchase low emissions vehicles to boost their image in their own social groups [17]. Secondly,
many studies find that consumers have a ‘wait and see’ attitude [18] in choosing new technologies, inparticular new automobile technologies. This is because consumers are risk averse and EVs present
uncertainty (e.g., range anxiety, availability of charging stations) when users are inexperienced. Thirdly,
consumers may have a particular preference towards one range of car model (e.g., brand, engine size,style). If their preferences are not satisfied with the available EV models, the technology (e.g., EV)will not match the preference for this group of consumers. When EVs become more popular with
consumers, this encourages the manufacturers to boost the number of EV models available, which
will further boost EV sales. As we will discuss in the next section, the FTT framework captures pathdependence and positive feedback effects in the diffusion process.
The following sections present an overview of the E3ME-FTT: Transport model. The values forthe initial parameters are presented in the Appendix A.
3.2. Structure of FTT: Transportation
The FTT framework models technological diffusion by a set of logistic differential equations of the
Lotka-Volterra family (The Lotka Volterra equations are a model of the population dynamics of species
competing for common resources (Lotka, 1925; Volterra, 1939)), which represent gradual technologicalsubstitution processes [19].

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Under the FTT framework, consumers are more likely to choose a technology that has a higher
market share as a result of social influence. The flow of market shares from technology j to technology i is
S j Si∆S j→i ∝F ∆Cji ∆t(1)
τ j
and the flow of market shares from technology i to technology j is
Si S jF ∆Cij ∆t(2)∆Si→ j ∝τ iwhere ∆S j→i denotes the flow of shares from car technology j to i, Fi ∆Cij denotes the fraction of
agents that prefer technology i over j based on the difference in generalized cost of technologies i andtechnology j, τ is the turnover rate for cars. The cost of technologies includes the car capital cost, fuelcost, maintenance cost and fiscal pricing (tax/subsidies), and an intangible variable that determines
the non-quantifiable cost for the consumers.
The diffusion processes are path dependent and involve positive feedbacks that are captured by
the FTT framework. The FTT models of technological diffusion are consistent with social influence
theory. It is assumed that decisions are recursive and self-reinforcing by assuming that social trends
play an important role in the diffusion of technology. In other words, if a group of consumers purchasea new technology through social influence, it is more likely that people around this group of consumers
will follow, leading to path dependence for a technology.
The technological diffusion rate is proportional to a comparison of cost distributions, equivalent
to a binary logit model. In addition to quantifiable costs (e.g., capital cost, fuel tax), the model considers
the non-quantifiable costs on consumer choices (e.g., comfort, luxury effect) by an arbitrary factor thatis added to the quantifiable costs. A learning rate is incorporated to take into account the falling cost
as production of the technology increases. Difference in the cost distribution is one of the key factors
in facilitating future technological diffusion. It is assumed that cost distribution corresponds to the
population heterogeneity, driving technological diffusion through comparison of cost distribution.
3.3. The Levelized Cost of Transportation (LCOT)The cost of the vehicle, as perceived by the investor purchasing a vehicle or unit of transporttechnology, must be taken to include all components relevant to the decision making. Many of thecomponents can be easily quantified with available data. Others are not straightforward, and we show
how this is done in Section 3.3.1. When a vehicle is purchased, an initial investment is made, or a loan
is obtained, for the capital cost, and henceforth fuel and maintenance costs are incurred for the lifetimeof the technology, in addition to taxation.
Following this, the Levelized Cost of Transport (LCOT) after the introduction of policies isdefined as
Ii + VTi + CT (αi ) + FUi (t) × FT (αi, t) + MRi + RTi (t)LCOTi = ∑(3)
(1 + t ) ttwhere,

•
•
•
•
•
•
•

VTi is a registration vehicle tax, in $/vehicle, paid at purchase time,
It is the capital cost of cars, in $/vehicle
CT (αi ) is the carbon tax based on fuel economy αi , in $/vehicle/(gCO2 /km)
FT (αi, t) is a tax on fuel consumption, in $/L
FUi (t) is the fuel consumption, in L/vehicle
MRi is the vehicle maintenance cost in $/vehicle
RTi (t) is a road tax in $/vehicle

3.3.1. The Generalized Cost as a Comparison Measure
The costs of transportation are not the only elements of consumer decisions when purchasing avehicle. Other aspects such as infrastructure and car range are valued by the consumer, but we cannot
obtain this information without a large-scale field survey or survey on the existing literature. We keep

Sustainability 2018, 10, 1612

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in mind that technologies have highly different pecuniary costs, particularly across engine size classes
and, despite this, higher costs can be compensated by higher benefits, such that higher cost luxury
vehicles maintain market shares.
Were we to simulate technology diffusion based on bare LCOT distribution comparisons, thelowest LCOT technologies would diffuse more successfully, which, as it turns out, is not consistent
with our historical data. Clearly, components would be missing in the LCOT, for instance comfort,
acceleration,style,
wePEER
may
call the ‘intangibles’. We define these ‘intangibles’ for9 ofthis
Sustainability
2018, that10, x FORREVIEW
31 modelas the difference between the generalized cost, which leads to observed diffusion, and the LCOT as
Were we to simulate technology diﬀusion based on bare LCOT distribution comparisons, thecalculated frompecuniary vehicle properties for which we have data. The value of the intangibles,
lowest LCOT technologies would diﬀuse more successfully, which, as it turns out, is not consistentdenotedγ,
isanempirical parameter that we obtain from making the FTT diffusion trajectory matchwithi our historical data. Clearly, components would be missing in the LCOT, for instance comfort,
the trajectoryobserved
in ourhistoricalat the yearof thethesestart‘intangibles’of the simulation.
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pairwisecomparisons
general vehicle
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For
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in lognormalidenotedγi, is an empirical parameter that we obtain from making the FTT diﬀusion trajectory match
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 data, at the year of the start of the simulation.
the trajectory observed in our historical2 the choice of investors is made based on
Taking into account the intangibles, we assume
LCOTthati γiq consistency withlni
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compare costs in lognormal
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𝐿𝐶𝑂𝑇 2∆LCOT 2i+ 𝛾
𝐶𝑖 =∆Cln i = ln(1 +𝑖
(4)
𝑖
(5)
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√𝐿𝐶𝑂𝑇 2 + Δ𝐿𝐶𝑂𝑇
i
𝑖

(

𝑖 )

Δ𝐿𝐶𝑂𝑇𝑖2
3.3.2. The Determination of Intangibles (γ)Δ𝐶 = √ln(1 +𝑖

𝐿𝐶𝑂𝑇𝑖2(5)

The technological diffusion rate is proportional to a comparison of cost distributions, equivalent
to a binary
logit
model. In of
additional
3.3.2. The
Determination
Intangiblesto(γ)quantifiable costs (e.g., capital cost, fuel tax), the model
considers the non-quantifiable costs (γ) on consumer choices (e.g., comfort, luxury effect) by an
The technological diffusion rate is proportional to a comparison of cost distributions, equivalent
arbitraryfactor that is added to the quantifiable costs. γ is a parameter that represents all unknown
to a binary logit model. In additional to quantifiable costs (e.g., capital cost, fuel tax), the model
constant
non-pecuniary
cost components
all costschoices
and benefits
not alreadyincluded).
considers
the non-quantifiable
costs (γ) (i.e.,
on consumer(e.g., comfort,
luxury explicitly
effect) by anThis parameterhas thevalue
that makestheγ diffusion
ratethat
continuous
the transition
arbitrary factor
thatuniqueis added
to theset
quantifiable
costs.
is a parameter
representsacrossall unknown
constant non-pecuniary
cost components
all ofcosts
benefits notalready
explicitly
included).diffusionfrom historical
data to simulated
data at the(i.e.,
starttheandsimulation(i.e.,
keeping
the existingThisparameterhas
theunique
value
set
that
makes
the
diffusion
rate
continuous
acrossthetrends). To illustrate this, when γi = 0, we obtain a rate of diffusion that does not transition
normally match
from historical data to simulated data at the start of the simulation (i.e., keeping the existing diffusion
historical diffusion (see Figure 1). This is because several factors such as comfort, luxurious effect andtrends). To illustrate this, when γi = 0, we obtain a rate of diﬀusion that does not normally matchthe availabilityof infrastructure
bebecause
readilyquantified
without
a large-scale
Therefore,
historical diﬀusion(see Figurecannot
1). This is
severalfactors suchas comfort,
luxurioussurvey.
effect andwe findthe
a set
of
γ
leads
to
the
diffusionoftechnology
inthesimulation
to
have
the
same
rate
availability
i of infrastructure cannot be readily quantified without a large-scale survey. Therefore, as the
we
find
a
set
of starting
γi leads topoint
the diﬀusionof technology(Figurein the simulation
to have the same rate as thehistorical rate at theof the simulation1).
historical
rate
at
the
starting
point
of
the
simulation(Figure1).
Although it does not provide significant information on the non-pecuniary benefits themselves,
Although it does not provide signiﬁcant information on the non-pecuniary beneﬁts themselves,
it is in this way
a robust methodology. We perform an exercise that determines that sensitivity of ourit is in this way a robust methodology. We perform an exercise that determines that sensitivity of oursimulation
results to the variation of γ (see the sensitivity analysis in Appendix C of this paper).
simulation results to the variation of γ (see the sensitivity analysis in Appendix C of this paper).

Figure 1. Cont.

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Figure1. Graphicalrepresentation
Note:Left
Left panels:
leftleftto thedashdashlines lines
show show
the historicalFigure 1.
Graphical
representationof of
γ. γ.Note:panels:thetheto thethe historicalfor petrolcar without
Theright
right toto theshow
the projectedsharessharesfor petrol
market market
shares sharesfor petrolcar without
γ. γ.The
thedashdashlines
lines
show
the projected
for petrolcars without γ. Right panels: the left to the dash lines show the historical market shares for petrol car
cars without
γ. Right panels: the left to the dash lines show the historical market shares for petrol carwith γ. The right to the dash lines show the projected shares for petrol cars with γ.
with γ. The right to the dash lines show the projected shares for petrol cars with γ.

3.3.3. Technology Learning

3.3.3. Technology Learning

Endogenous technical change is assumed in the E3ME and FTT models, both at the aggregate
level
in and throughlearningby doing.
The FTT:model
curves,capitalEndogenous
technical
changeis assumed
in Transport
the E3ME
anduses
FTTlearningmodels,bothandat theaggregatecosts for vehicle technologies (𝐼𝑖 (𝑡)) fall by a certain percentage (learning rate 𝑏𝑖 ) every time the total
level in and through learning by doing. The FTT: Transport model uses learning curves, and capitalquantity manufactured 𝑊𝑖 (𝑡) doubles:

costs for vehicle technologies (Ii (t)) fall by a certain percentage (learning rate bi ) every time the total
𝑊𝑖 (𝑡) −𝑏
quantity manufactured Wi (t) doubles:
𝐼𝑖 (𝑡) = 𝐼0,𝑖 (
) 𝑖
(6)
𝑊0,𝑖

− bi
The learning rate is the cost reduction achievedWfora doublingof cumulative installed capacity.
i (t)
)
(6)
I
t
=
I
(
(
)
0,i
For niche technologies, the existence i of technologicallearning
impliesthat prices for the new
W0,itechnologies (e.g., electric cars) will fall as the quantity of production increases. Note that the learningfor niche
are subjectachieved
to great uncertainty(see of[20,21])
as a resultof different
Therateslearning
ratetechnologies
is the cost reduction
for a doubling
cumulative
installed
capacity. For
methods of estimation. Additionally, for some technologies, specific learning curves and productniche technologies, the existence of technological learning implies that prices for the new technologies
ratios have been observed in the past, but the ratio may not be applicable for the future since learning(e.g., electriccars)change
will fallthe [22].
quantity of production increases. Note that the learning rates for nicherates mayoverastime
technologiesToare
subject
great
uncertainty(see [20,21])a resultof different
methods
of estimation.
account
forto
theuncertainties
regardinglearning as
rates,
a sensitivity
analysis
is presentedinAppendix
where
we examinespecificthe extent
to whichthe anddifference
in ratios
learninghave
rate beencreates
Additionally,
for C,
some
technologies,
learning
curvesproduct
observed
uncertainties
themaymodel.
studies may
using
a stochastic
in the past,
but the for
ratio
notFurther
be applicablefor consider
the futuresince
learningmodel
ratesformulation,
may change overwhich can explicitly calculate the impact of learning rate uncertainties in a rigorous manner.

time [22].
To 3.4.
account
the uncertainties
regarding learning rates, a sensitivity analysis is presented in
Energyfor
Consumption
and Emissions
Appendix C, where we examine the extent to which the difference in learning rate creates uncertainties
The total service generated by a particular technology is equal to the product of transport
for the model.
Further studies may consider using a stochastic model formulation, which can explicitly
demand in pkm and the transport capacity of the technology, deﬁned as the number of seats in cars
calculatetheimpactof learning
rate uncertainties in a rigorous manner.
or public transport,thus we have:
3.4. Energy Consumption and Emissions

𝐺𝑖 =𝑈𝑖 × 𝐶𝐹𝑖(7)

where 𝐺𝑖 is the service generated by a transport technology, in pkm/year.

The total
service generated by a particular technology is equal to the product of transport demand
The energy consumption equation is calculated from fuel consumption per km and the services
in pkm theandvehicles
the transportcapacity ofthethetechnology,
as the number of seats in cars or public
provide, considering
ﬁllingfactor 𝐹𝐹𝑘defined,transport, thus we have:
𝐽𝑘,𝑡 = 𝐺𝑘,𝑡 × 𝐸𝐺𝑘,𝑡 /(𝐹𝐹𝑘 × 𝑁𝑠𝑒𝑎𝑡𝑠 )(8)
Gi =Ui × CFi(7)
where 𝑁𝑠𝑒𝑎𝑡𝑠 is the number of seats for cars (i.e., 𝑁𝑠𝑒𝑎𝑡𝑠 = 4 in most cases), J is energy consumption

where Gi is the service generated by a transport technology, in pkm/year.
in MJ/year, 𝐸𝐺𝑘,𝑡 is the energy consumption factor in MJ/seat-km, calculated by a fuel economy
The energy consumption equation is calculated from fuel consumption per km and the services
the vehicles provide, considering the filling factor FFk ,Jk,t = Gk,t × EGk,t /( FFk × Nseats )(8)

where Nseats is the number of seats for cars (i.e., Nseats = 4 in most cases), J is energy consumption in
MJ/year, EGk,t is the energy consumption factor in MJ/seat-km, calculated by a fuel economy factor

Sustainability 2018, 10, 1612

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2 multiplied by energy densities 3 for petrol/diesel/ethanol. CO2 emissions from passenger vehicles
are closely related to energy consumption. Emissions are defined as:
Ek,t = Gk,t × CO2k,t(9)

where Ek,t is the fleet emissions in Gt/yr and CO2k,t is the emissions factor.
3.5. Linkage between FTT: Transport and E3ME
The E3ME model is a non-equilibrium macro econometric simulation model based on ademand-led post-Keynesian structure [23], which implies a non-equilibrium simulation framework.
In the post-Keynesian world, models are simulations, and productivity change takes place throughknowledge accumulation, using Kaldor’s technology progress functions [24] (for more details
see [23,25]). The model is demand-driven. Endogenous technical change is considered in the E3ME
model with a progress indicator. With a chosen set of econometric relationships, the model regresses
over a high dimensional dataset covering the past 40 years in 59 regions and extrapolates these
relationships up to 2050 (see further details in the E3ME manual) (http://www.e3me.com).
In contrast to the computable general equilibrium (CGE) approach where optimal behavior isassumed, in the E3ME model, the determination of output follows post-Keynesian theory. Morespecifically, prices are not always adjusted to market clearing level like the CGEs. Hence, there could
be spare capacity as a result of regulations and increased economic output.
The E3ME model has a strong empirical grounding, with a historical database that covers theperiod 1970–2014. The E3ME model uses a system of error correction which allows short-term dynamic
outcomes, moving towards a long-term trend [26]. Energy demand is a function of economic activity
rates, relative prices, and technology. The transport activity in the E3ME model is determined byfuel prices, car prices and income. Emissions are calculated using the transport demand linked to thetechnology diffusion model (the FTT model).
The FTT model is fully integrated to E3ME with several dynamical feedbacks to the global
economic simulation. The combination of the E3ME-FTT transport model and the E3ME-FTT powermodel provides a relatively high definition of dynamical coverage of global fossil fuel use andemissions [23].
The FTT framework captures path dependence and positive feedbacks. Here, it is assumed thatagents may not be fully rational and only know a subset of information. Compared to the traditionalmodels, the E3ME-FTT framework models technological transition incorporating complexity, pathdependence and consumer heterogeneity, with low computational cost.
The dynamic interactions between the FTT model and the E3ME model are shown in Figure 2.
Purchase decisions are affected by four components, including consumer preference, government
policies, market environment, and the car model’s availability. Each of the components leads to a
dynamic change of market share using the Future Technology Transformation (FTT) framework. The
Transport demand is calculated within the E3ME model by regressing total vehicle use (in km/year)with respect to fuel prices and income, and these are projected to 2050, using fuel prices and income
endogenously determined by E3ME. The number of cars is regressed against income and averagevehicle prices, constrained by population. In the following sections, we describe the components
within the FTT model, as well as the dynamics and interactions between components.

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Figure 2.
2. Structure
Structure ofof E3ME-FTT:
E3ME-FTT: Transport
TransportModel.
Model.
Figure4. Policy Assumptions
4. Policy Assumptions
We have assessed four decarbonization scenarios that combine taxes, kick-start programs andWe have assessed four decarbonization scenarios that combine taxes, kick-start programs andbiofuel mandates. The definition of the policy incentives is listed in Table 5. The policy assumptions
biofuel mandates. The definition of the policy incentives is listed in Table 5. The policy assumptions areare based on the current and proposed policy incentives as discussed in Section 2. The policy
based on the current and proposed policy incentives as discussed in Section 2. The policy assumptions
assumptions are presented in detail in Appendix B.
are presented in detail in Appendix B.
Table 5. Definition of policy incentives.
Table 5. Definition of policy incentives.

PolicyPolicyIncentives
Incentives

Model Representation

Vehicle tax

Added/subtracted to the capital cost at the time of car purchases

Acquisition
tax, EV
Acquisition
tax, EV rebates

Annual registration tax

Added to the annual costs summed to get the LCOT

rebates
Road tax

Vehicle tax

Annualregistration
tax
Carbon tax
CarbonFueltax
tax
Biofuel mandatePhase
outtaxregulation
Fuel

Model Representation

Examples of the RealExamples of the World
Real-World
PolicyPolicyAdded/subtracted to the capital cost at the time of car purchases

Same asAdded
vehicle totax,thethisannual
is a taxcosts
on expected(nottoyetgetemitted)CO2
summedthe LCOT
Road tax
emissions. The tax is proportional to fuel economy in the unit ofAcquisition tax based on fuel economy
USD/(gCO/km)
Same as vehicle tax, thisis a tax 2on
expected (not yet emitted) CO 2

Acquisition tax based
emissions. The tax Added
is proportional
fuel economy in the unit ofFuel tax (e.g., petrol tax, diesel tax)
to the fuelto
cost
on fuel economy
USD/(gCO
Biofuel is a certain
percentage2/km)
of liquid fuels
Biofuel mandate
Fuel tax (e.g., petrol tax,
The sale of lower efficiency
Fuel economy standards
Addedliquid
to thefuelfuelvehicles
cost is banned
diesel tax)
A certain percentage of EVs are bought by someone or some
Biofuel is a certain percentage of liquid fuels
Biofuel mandate

Biofuel mandate
Kick-start program
institution (e.g., public or private institutions) as a policy
Government-financed purchases
Phase out
or strategy.
The sale of lower efficiency
liquid fuel vehicles is banned
Fuel economy standards
regulation
Kick-start
A certain percentage of EVs are bought by someone or some institution
Government-financed
The assumptions for
thepublic
scenario
analysisare presented
Appendix B. The baseline
scenario
program(e.g.,or private
institutions)as a policy orinstrategy.
purchases

makes projections based on the current policy framework. It is the benchmark scenario against which
the other
are compared.
Scenario
1 assumesthat all taxinincentives
areB.combinedwithscenario
a small
The scenarios
assumptions
for the scenario
analysisare presented
Appendix
The baseline
kick-start
program.based
The levels
tax incentives
that we assumed
in Scenarioscenario
1 are theagainst
averagetax
makes projections
on the ofcurrent
policy framework.
It is the benchmark
whichvalues
taken
from
each
of
the
four
East
Asian
countries.
Itshould
be
noted
that
we
have
includedthe other scenarios are compared. Scenario 1 assumes that all tax incentives are combined with athecostof licenseprogram.
plate registration
the vehicle
kick-start
program1 in
1 is
small
kick-start
The levelsasofpart
tax ofincentives
thattax.
weTheassumedin Scenario
areScenario
the averageassumedbe 5%fromhigherthan
thefournumberEVs countries.
on the road2018bein noted
China.that we have included
tax valuestotaken
eachof theEastofAsian
It byshouldScenario
2 assumesthat tax incentives
combined
regulations
andprogram
the kick-start
program.
the costof license
plate registrationas partareof thevehiclewith
tax. The
kick-startin Scenario
1 isWeassumetothat
the higher
acquisition
increasedby 3000
USD.
Theby
levelsare set according
assumedbe 5%thantax
theisnumberof EVson theroad
2018ofinregulations
China.
to theScenario
expected 2fueleconomystandards
as shownarein Table
1. We withassumethat the kick-startisassumesthat
tax incentives
combined
regulations
and theprogram
kick-startmuchstrongerthan it isin Scenario
1.
program.
We assumethatthe acquisition
tax is increased by 3000 USD. The levels of regulations are
Scenario 3toassumes
that regulations,thekick-startasprogram
the1.biofuel
mandateset according
the expectedfuel economy
standards
shown inandTable
We assume
thatprogram
the kickare
added
on
top
of
the
taxes.
The
biofuelmandate
is
set
based
on
IEA
WEO
2014
assumptions.
The
start program is much stronger than it is in Scenario 1.
scenario
exploresthe effectivenessof the policy
incentivesprogramin cuttingand
emissionsfrom mandate
private passenger
Scenario
3 assumesthat regulations,the kick-start
the biofuel
programare added on top of the taxes. The biofuel mandate is set based on IEA WEO 2014 assumptions. The
scenario explores the effectiveness of the policy incentives in cutting emissions from private

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vehiclesandvehicles
demonstrates
the extent tothe
whichlayersof fiscal
policyandmeasures
kick-start andprogrampassenger
and demonstrates
extentto whichlayersofmeasures
fiscal policy
kickwill
leadtoasignificant
emissions
reduction.
start program will lead to a significant emissions reduction.
ItItisisnoted
notedthat
thatthethetaxtaxincentive
incentiveassumptions
assumptionsand
andsubsidies
subsidiesare
arehigher
higherthan
thanthethelevellevelofofexisting
existing
policyincentives
in
East
Asian
countries.
A
kick-start
programisassumedinevery
scenario
policy incentives in East Asian countries. A kick-start program is assumed in every scenarioto
totaketake
intoaccount
current
non-monetary
programs(e.g.,
EVcar
sharing,EVlicenseplate
auctions)
that
into account current non-monetary programs (e.g., EV car sharing, EV license plate auctions)have
that
been
introduced
atnational
level
toencourageEVdiffusion.
have been introduced at national level to encourage EV diffusion.
5. Data Overview
5. Data Overview
We collected car sales data (for each model) from the Marklines data, which is then matched
We collected car sales data (for each model) from the Marklines data, which is then matchedwith car price data and car engine size data, taken from the car manufacturers’ websites (in 2012).
with car price data and car engine size data, taken from the car manufacturers’ websites (in 2012).
Figures 3 and 4 show the 2012 emissions distribution and car prices distribution for China, Japan,
Figures 3 and 4 show the 2012 emissions distribution and car prices distribution for China, Japan,
Korea and Taiwan. Sales of alternative technologies, hybrid and electric cars, are shown in pink andKorea and Taiwan. Sales of alternative technologies, hybrid and electric cars, are shown in pink andred, respectively.
red, respectively.
The distributions show the diversity of consumers in car choices for each country. It is notable that
The distributions show the diversity of consumers in car choices for each country. It is notable
the price distribution in China is much wider than in Japan, Korea and Taiwan. In particular, the pricethat the price distribution in China is much wider than in Japan, Korea and Taiwan. In particular, theand emissions distributions are much narrower for Korea and Taiwan. This implies that consumer
price and emissions distributions are much narrower for Korea and Taiwan. This implies thatchoices are more diverse in China, with most people showing preference for the small/medium-size
consumer choices are more diverse in China, with most people showing preference for thevehicles. Regarding emissions distribution, due to consumer choices of car sizes and fuel efficiency,
small/medium-size vehicles. Regarding emissions distribution, due to consumer choices of car sizes
average emissions are lower in China and Japan, with the standard distribution of emissions muchand fuel efficiency, average emissions are lower in China and Japan, with the standard distribution
larger in China than in Japan.
of emissions much larger in China than in Japan.

Figure 3.3. Emissions
Emissions distributions
distributions forfor thethe fourfourEast
EastAsian
Asiancountries.
countries. Note:
Note: The
The distributions
distributions for
for
Figurealternativefuelfuelvehicles
vehiclesare
areshown
shownininpinkpinkand
andred.
red.The
Theleft
leftaxis
axisshows
showsthetheemissions
emissionsdistribution
distributionfor
foralternative
conventional
cars,
and
the
right
axis
shows
the
emissionsdistributionforhybridcars.
conventional cars, and the right axis shows the emissions distribution for hybrid cars.

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Figure 4.4. Price
Price distributions
distributions ofof 20122012 vehicles
vehicles sales
sales forfor thethefourfourEast
EastAsian
Asiancountries.
countries. Note:
Note: The
The
Figuredistributions
foralternative
fuelvehicles
are
shown
in
pink
and
red.
The
left
axis
shows
the
sales
distributions for alternative fuel vehicles are shown in pink and red. The left axis shows the sales
distribution forfor conventional
conventional cars,
cars, andand thethe right
right axis
axis shows
shows thethe price
price distribution
distribution forfor new
new energy
energydistribution
vehicles,such
ashybridcars
andelectric
cars.
vehicles, such as hybrid cars and electric cars.

6. Results
Results
6.
6.1. China
China
6.1.
In thethebaseline
baselinescenario,
scenario,wewe
find
share
for increases
EVs increases
very slowly,
reachingIn
findthatthat
thethefleetfleet
share
for EVs
very slowly,
reaching
below
below
10%
of
the
fleet
share
in
2050
(Figure5).
10% of the fleet share in 2050 (Figure 5).
In scenario
scenario 1,
1, we
we assume
assume thatthat thethe excise
excise duty
duty isis increased
increased byby 1200
1200 USD
USD per
per vehicle,
vehicle, thethe annual
annualIn
registration tax
tax isis increased
increasedby
by300
300USD,
USD,andandthethefuelfueltax
taxisisincreased
increasedby
by1.2
1.2USD/L.
USD/L. We
We assume
assume that
that
registration
thesubsidyforEVsisincreased
by
up
to
9000
USD.
There
is
a
kick-start
programof
0.3%,
meaning
the subsidy for EVs is increased by up to 9000 USD. There is a kick-start program of 0.3%, meaningthat there
there will
will bebe 600,000
600,000 EVs
EVs onon roadroad byby 2020.
2020. AsAs shown
shown inin Figure
Figure 5,5, eveneven with
with these
these incentives
incentives totothatencourage EV
EV diffusion,diffusion,thethefleetfleetshare
sharefor
forEV
EVonlyonlyincreases
increasesto
toaroundaround30%
30%inin2050.
2050.
encourageTo
cutemissionssignificantly
andtoencouragethediffusion
of
EV,
we
have
to impose
impose both
both tax
tax
To cut emissions significantly and to encourage the diffusion of EV, we have toincentives, regulations
regulations andand aa stronger
stronger kick-start
kick-start program
program toto facilitate
facilitate thethe diffusion
diffusion ofof zero-emission
zero-emission
incentives,
vehicles.
In
Scenario2,
weassume
that
there
are
an
additional
2,000,000
EVs
on
the
road
2020,
vehicles. In Scenario 2, we assume that there are an additional 2,000,000 EVs on the road byby
2020,
asas
a
a
resultofvariouskick-start
programs(e.g.,
carsharing
programs,
licenseplate
auctions,
publicEV
result of various kick-start programs (e.g., car sharing programs, license plate auctions, public EV
mandates). A
A fuelfuel economy
economy standard
standard isis imposed
imposed soso thatthat old
old gasoline
gasoline car
car models
models (car(car models
models before
beforemandates).
2012)
are
phasedout.
Scenario
3(column
3)
shows
the
passenger
cars
emissionsasaresultofthe2012) are phased out. Scenario 3 (column 3) shows the passenger cars emissions as a result of fueltheeconomyregulations
andandthethephase
outoutpolicies.
The
shares2050 and
and
fueleconomyregulations
phase
policies.
The
sharesfor
forEVsEVsreachreachover
over70%70% byby 2050emissions peak
peak aroundaround2030,
2030,before
beforefalling
fallingto
tothe
the2005
2005level
levelbyby20502050(Figure(Figure5).
5).
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Figure 5.
5. Passenger
Passenger transporttransport demand
demand fromfrom passenger
passenger vehicles
vehicles forfor thethe fourfour East
East Asian
Asian countries
countries under
under
Figurefour
policyscenarios.
Note:
Column1
shows
thebaseline
scenario.
Column2
showsthescenario
four policy scenarios. Note: Column 1 shows the baseline scenario. Column 2 shows the scenario when
when vehicle
tax, registration
tax,
fuel
tax, akick
small
kick
start
and carbon
are imposed.
vehicletax, registration
tax, fueltax,
a smallstartandcarbontax are tax
imposed.
ColumnColumn
3 shows3shows
the scenario
when
tax incentives
are combined
fuel economy
regulations
and akick-startstrongerthescenario
when tax
incentives
are combinedwith fuelwith
economyregulations
and a stronger
kick-start
program.
Column4showsthescenariowhen
tax
incentives,
regulations
and
a
kick
start
program. Column 4 shows the scenario when tax incentives, regulations and a kick start programisprogramiscombinedwithabiofuelmandate.
combined with a biofuel mandate.

6.2. Japan
6.2. Japan
We findfind thatthat inin thethe baseline
baseline scenario
scenario passenger
passenger car
car emissions
emissions inin JapanJapan fall
fall byby moremore than
than 40%
40%
We
below the 2005 emissions level, without any additional policy incentives, due to the diffusion of
below the 2005 emissions level, without any additional policy incentives, due to the diffusion of hybrid
hybrid cars in Japan (Figure 5).
cars in Japan (Figure 5).
As aa result
resultof
ofvarious
variousvehicle
vehicletaxes
taxesandanda asmall
smallkick-start
kick-start
programthat
adds
180,000
EVs
(0.3%
As
programthat
adds
180,000
EVs
(0.3%
of
of
the
Japanesefleet)
by2020,
passengercaremissions
fall
by
50%
below
the
2005
emissionslevel
bythe Japanese fleet) by 2020, passenger car emissions fall by 50% below the 2005 emissions level by 2050.
2050. In scenario 1 (column 2), we assume that the automobile acquisition tax for gasoline cars
In scenario 1 (column 2), we assume that the automobile acquisition tax for gasoline cars increases by
increases by up to 1200 USD for luxury vehicles and the fuel tax by up to 1.2 USD/L. The EV rebate
up to 1200 USD for luxury vehicles and the fuel tax by up to 1.2 USD/L. The EV rebate is increased byis increasedaround9000 USD
pertovehicle(for upAstoaeight
result of thearound9000 byUSD
per vehicle(for upeight years).
resultyears).
of the As
tax aincentives
andtaxEV incentives
subsidies,andEV
subsidies,theshares
for
EVs
reach
30%
in
Japan
by
2050.
the shares for EVs reach 30% in Japan by 2050.
To cut passenger car emissions further (by more than 50% below 2005), we assume that
To cut passenger car emissions further (by more than 50% below 2005), we assume that compared
compared to the base year (2012), there are 900,000 EVs on the road by 2020 as a result of various
to the base year (2012), there are 900,000 EVs on the road by 2020 as a result of various kick-start
kick-start programs (e.g., increasing the number of charging stations, EV car sharing). We assume
programs (e.g., increasing the number of charging stations, EV car sharing). We assume that thethat the previous gasoline car models will be phased out as a result of the top runner program. The
previous gasoline car models will be phased out as a result of the top runner program. The fleet share
fleet share for EVs increases by a further ten percentage points by 2050, reaching 40% fleet share.
for EVs increases by a further ten percentage points by 2050, reaching 40% fleet share. Emissions are
Emissions are reduced by nearly 80% below the 2005 level in scenario 2. In scenario 3, when a biofuel
reduced by nearly 80% below the 2005 level in scenario 2. In scenario 3, when a biofuel mandate
mandate is introduced in addition to the tax incentives, fuel economy standards, and a kick-start
program, emissions fall by nearly 90% below 2005 levels (Figure 6).

Sustainability 2018, 10, 1612

16 of 32

is introduced in addition to the tax incentives, fuel economy standards, and a kick-start program,emissions
90% REVIEW
below 2005 levels (Figure 6).
Sustainability fall
2018,by
10,nearly
x FOR PEER
16 of 31

6. Private
Private passenger
passenger vehicles
vehicles emissions
emissions (MtCO(MtCO22/yr)/yr) forFigure 6.
for thethe fourfour East Asian countries underfour policy
1 shows
thethebaseline
scenario.
Column2 shows
the scenario
whenpolicyscenarios.
scenarios.Note:
Note:Column
Column1 showsbaseline
scenario.
Column2 shows
the scenario
vehicletax, registration
tax, fueltax,
a smallstartandcarbontax are tax
imposed.
Column3 shows
when vehicle
tax, registration
tax,
fuel
tax, akick
small
kick
start
and carbon
are imposed.
Column3
thescenario
when tax
incentives
are combinedwith fuelwith
economyregulations
and a strongershows
the scenario
when
tax incentives
are combined
fuel economy
regulations
and akick-startstrongerprogram.
shows thescenariowhen
tax incentives,
regulationsregulations
and a kick-startiskick-start Column
program.4 Column
4 shows
thescenario
when tax incentives,and aprogram
kick-start
combineda biofuelmandate.
program iswith
combinedwitha biofuel mandate.

6.3. Korea

the baseline
baselinescenario,
scenario,luxury
luxurydiesel
dieselcars
carsandandgasolinecars
have
a largemarketshare
beforeIn thegasolinecars
have
a largemarketshare
before
the
the
year
2030
(Figure
3).
After
2030,
luxuryhybrid
cars
start
to
take
off,
and
reach
more
than
60%
year 2030 (Figure 3). After 2030, luxury hybrid cars start to take off, and reach more than 60% market
market2050. However,the penetrationof hybridcars is nottosufficient
to cutinsharebyshare2050. byHowever,the penetrationof hybrid cars
is not sufficient
cut emissions
in emissions
South Korea,
South
Korea,as shown
in Figurecontinue
4. Emissions
continue
to increase
the baseline
scenario,a resultasshown
in Figure4. Emissions
to increasein thebaselinein
scenario,
as a resultof aasrapid
riseof vehicle
a rapid numbers
rise in vehicle
numbersin 6).
Korea (Figure 6).
inin Korea(Figure
USD/L
and
that
there
is an
In scenario 1, we
we assume
assumethat
thatthere
thereisisan
anincrease
increaseininthe
thefuelfueltax
taxofof1.2
1.2
USD/L
and
that
there
is
increaseingasolinecartaxation
by
up
to
1200
USD
for
petrolcars.
Weassume
that
there
is
an
an increase in gasoline car taxation by up to 1200 USD for petrol cars. We assume that there
additional subsidy
subsidythat
thatsupports
supportsEVs,
EVs,amounting
amountingtotoUSD
USD9000
9000forforluxury
luxuryEVs.
EVs.WeWe
assumethat
there
additional
assume
that
there
is
a kick-start
programthatadds
adds126,000
126,000EVs
EVson
onthe
theroadroadbyby2020.
2020.As
Asaa result
result ofof thethe policy incentives,
aiskick-start
programthat
the shares for EVs increase to around 40% of the market by 2050 in scenario 1. Emissions are reduced
50% below
below thethe 2005
2005 level.
level.
by more 50%
To increase
increase thethe rate
rate ofof diffusion
diffusion ofof EVsEVs inin SouthSouth Korea,
Korea, we
we assume
assume that the numbers for EVs
To
ofofa kick-start
program.
As As
shown
in Figure3, theincrease by 190,000
190,000 on
onthe
theroadroadbyby20202020asasa aresult
resulta kick-start
program.
shown
in Figure3,
sharesfor EVs
reachreachoverover50% by2050.
As a result,emissionsfall by fall
80%by
below
2005thelevel(Figurethesharesfor EVs50%
by 2050.
As a result,emissions
80% the
below
2005
level
6). To cut
emissionsfurther,
we assumethat thatthe thebiofuelmandatecontinues
totoincrease(Figure6). Tocut emissions
further,
we assume
biofuelmandatecontinues
increasein
in Korea,
Korea,
reaching 22% by the year 2050. With the biofuel mandate in place, in scenario 4, emissions fall by 90%
below the 2005 level (Figure 6).

Sustainability 2018, 10, 1612

17 of 32

reaching 22% by the year 2050. With the biofuel mandate in place, in scenario 4, emissions fall by 90%
below the 2005 level (Figure 6).
6.4. Taiwan
Unlike the other countries (e.g., Japan and Korea), the baseline shares for hybrid cars and EVs
remain very small in Taiwan. The small shares in new energy technologies result in a very small fall inpassenger vehicle emissions and energy consumptions in the baseline scenario.
When various tax incentives are added (as shown in Table A8 scenario 1) and the presence of a
kick-start program (that places 20,000 EVs on the road by 2020), we observe that emissions fall slightly,
by around 25% below the 2005 level. To cut emissions by 60% below the 2005 level, it is necessary to
introduce more zero-emission vehicles and a biofuel mandate in Taiwan. In scenario 2, we assume that
there would be a 1.5% EV share in 2020, meaning that there would be an additional 100,000 EVs in
Taiwan. To cut emissions further in Taiwan, in scenario 3, we assume that the biofuel mandate rises to
20% by 2050.
7. Policy Implications and Conclusions
The decarbonization of the private passenger vehicles sector plays an important role in increasing
our chance of staying within the 2 ◦ C target set by the Paris Agreement. In this paper we assessed
four scenarios that examine the interactive effect of transportation policy incentives on emissions
reduction in private passenger vehicles in four East Asian countries. For all the countries, we find thatan integrated approach of tax incentives, subsidies, regulations (fuel economy efficiency), kick-start
programs and a biofuel mandate yield the most significant emission reductions, by allowing the
diffusion of EVs. Other than taxes on conventional cars and subsidies for EVs, it is important tointroduce non-monetary measures, so as to increase the rate of diffusion for zero emissions vehicles in
the region.
We find that in the case of China, in the baseline scenario, EVs do not gain significant sharesby 2050 without any policy incentives. However, an integrated approach (combining tax incentives,
subsidies and kick start programs) will lead to more than 80% EV share in China by 2050. In reality,
in addition to EV subsidies and taxes on gasoline cars, some kick-start programs have been introduced
to increase the rate of EV diffusion in China.
For Japan, it is possible to achieve over 40% emission reductions without any policy incentives.
Emissions from passenger vehicles can be reduced by over 50% with a higher acquisition tax and ahigher EV rebate. With 900,000 EVs on the road (compared to the 2012 level), it is possible to achieve
over 90% emissions reduction below the 2005 levels.
Similarly, for Korea we find that the tax incentives are not sufficient to cut emissions significantly.
The diffusion of hybrid cars in South Korea will not lead to a significant emissions reductions, becauseof an increase in the car population. To cut emissions by more than 50% below the 2005 level, it isimportant to encourage the diffusion of EVs in South Korea by a kick-start program that increases thenumber of EVs by around 190,000 on the road (compared to the 2012 level) by 2020. Further emission
reductions can be achieved with the introduction of a small biofuel mandate (around 20%).
In the case of Taiwan, without a kick-start program and fuel economy regulation, emissions frompassenger cars will not be cut significantly. Due to the existing small number of EVs and hybrid cars in
Taiwan, compared to other Asian countries, from the scenario analysis we find that the diffusion of
new energy technology is slower. In order to increase the rate of diffusion of new energy technologies
in Taiwan, it is necessary to introduce the EV kick-start program that increases the number of EVs
in Taiwan.

Sustainability 2018, 10, 1612

18 of 32

A major barrier to introducing taxes, regulations and the kick-start program is the cost of thepolicies to the government, the car manufacturers and car owners. The true cost/benefit of policy
incentives is difficult to estimate, given that the disruptive impact of new energy vehicles on theeconomy and on the car manufacturers is unclear. For example, while the introduction of new energy
vehicles could potentially create new employment, some car manufactures may be unable to compete in
the market. It is important for further research to investigate the economic impact of policy incentives
that encourage the diffusion of new energy technologies.
8. Limitations and Recommendations
Like all modelling studies, we recognize there are limitations to our approach in modelling futuretechnological change and emissions from cars. This section identifies the main limitations encounteredand how improvements can be made in future studies:
(1)

(2)

(3)

In this research, we have identified the available technologies, although new technologies will
emerge in the future. However, it is impossible for this model to predict technologies that havenot penetrated into the market, for example, fuel cell vehicles.
The non-pecuniary cost is represented by a γ parameter, which is found by calculating thedifference between the historical shares and future shares. Our projections from 2012 to 2020
follow the trend from 2004 to 2012. However, γ parameters for EVs in Taiwan and South Korea
may carry a degree of uncertainty, because our basis to determine the γ parameter is not sufficient,
based on the fact that there is currently a limited number of EVs on the road. A better calibration
can be achieved with the EV market shares data obtained from recent years (2012–2017).
Autonomous vehicles (AVs) represent a disruptive technology that could potentially impact on
the vehicle size and transport demand for passenger cars.

In addition, there are several parametric uncertainties for the E3ME-FTT-Tranpsort model. We
take into consideration the possible consequences of these assumptions and perform a sensitivity
analysis to determine the effects of the potential changes to the learning rate, the γi values, the discountrate and fuel prices on the final results. The sensitivity analysis provides insight into the effects ofuncertainties on our projections.
As shown in Appendix C, we allow the parameters to vary by between 5% and 20%. The effect ofthe parametric uncertainties on the market shares and emissions projections depends on the scenario
assumptions and the technology mix in a country. For example, the effect of the technology learning
rate is more prominent as the shares for EVs increase. We observe that certain parameters, such asfuel prices and discount rates, may have a larger effect on shares and emissions than other parameters,such as EV learning rates. This is because while learning rates only impact on total emissions throughvariations in EV shares, a rise or fall in oil prices affect vehicle choices dynamically. We find that theuncertainties in γi values have an overall small impact on the final results. Overall, the changes inmarket shares and emissions as a result of the variations in parameters are within 20%.
Author Contributions: A.L. and S.L. co-designed the scenarios. A.L. parameterized the model and wrote themain text. S.L. contributed to the text. Y.C. and C.-H.L. provided data for S. Korea and Taiwan. J.-F.M. designed
the theoretical model. U.C., S.B. and H.P. designed and executed the econometric specifications for transportdemand, maintained the E3ME model and contributed to the text.
Acknowledgments: The authors wish to thank the The Society for Environmental Economics and Policy Studies
SEEPS conference participants for lively discussion on the subject. We thank three anonymous referees for theirsupportive comments. The authors acknowledge C-EERNG and Cambridge Econometrics for support, andfunding from EPSRC (JFM, fellowship no. EP/ K007254/1); the Newton Fund (JFM, HP, UC, SB EPSRC grant no
EP/N002504/1 and ESRC grant no. ES/N013174/1), NERC (HP, UC, grant no. NE/P015093/1).
Conflicts of Interest: The authors declare no conflict of interest.

Sustainability 2018, 10, 1612

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Appendix A. Initial Parameters
Tables A1–A4 show the values of the parameters assumed in the model for China, Japan, Korea
and Taiwan respectively. Car prices, engine sizes and fuel economy data for each car model listed indata from the company Marklines (We have purchased the annual car sales data from the Marklines
website in the year 2013 and 2014, which is an automotive industry portal that provides of motor
vehicles market data. Marklines provides for the total car sales by car model and brand for 63 countries
from 2004 onwards. Hence, it is possible to know the sales for each car model name for individual
countries. https://www.marklines.com/en/vehicle_sales/index). Price data and fleet populations
were are collected from various sources, including car manufacturers, car sales websites, car industry
market reports and government institutions, matched to the car models listed in the Marklines data.
Note that the prices obtained are the listed price in the year 2013 when the data were collected. Car
fuel economy data were collected from the manufacturers’ websites when available. In many cases,
some car models had several car prices and fuel economy values, depending on vehicle options forparticular car models. We took the average value for prices and engine sizes, unless it was known
to us that a particular vehicle option/alternative was more popular than the others. To ensure thereliability of the data outside the manufacturer’s website, we checked the prices, engine sizes and fueleconomy data from these car sales, research websites and government institutions against the dataobtained from the manufacturers.
Learning rate and the discount rates are subject to a certain degree of uncertainty. To account for
the uncertainties regarding learning rates, a sensitivity analysis is presented in the Appendix C where
we examine the extent to which the difference in learning rate and discount rate creates uncertainties
for the model.

Sustainability 2018, 10, 1612

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Table A1. The initial parameters assumed for China.
Technology

Engine Size

Prices of Cars
(USD/vehicle)

Standard Deviation ofPrice (USD/vehicle)

Fuel Cost(USD/km)

Discount Rate

Energy Use (MJ/vkm)

Learning Rate

Petrol

Econ
Mid
Lux

9400.00
21,036.00
40,667.00

6249.00
12,005.00
20,083.00

0.07
0.08
0.10

0.15
0.15
0.15

2.05
2.26
2.74

1%
1%
1%

Adv Petrol

Econ
Mid
Lux

9400.00
21,036.00
40,667.00

6249.00
12,005.00
20,083.00

0.05
0.06
0.08

0.15
0.15
0.15

1.64
1.81
2.19

5%
5%
5%

Diesel

Econ
Mid
Lux

9400.00
22,000.00
40,300.00

1000.00
5631.204404.40

0.05
0.06
0.07

0.15
0.15
0.15

1.85
2.12
2.40

1%
1%
1%

Adv Diesel

Econ
Mid
Lux

9400.00
22,000.00
40,300.00

1000.00
5631.204404.40

0.04
0.05
0.06

0.15
0.15
0.15

2.95
1.70
1.92

5%
5%
5%

CNG

Econ
Mid
Lux

9635.00
13,953.00
33,710.00

1965.00
2654.00
2654.00

0.04
0.05
0.06

0.15
0.15
0.15

1.50
1.70
2.09

1%
1%
1%

Hybrid

Econ
Mid
Lux

31,252.91
41,018.00
47,584.00

1654.00
1654.00
1571.00

0.02
0.02
0.02

0.15
0.15
0.15

0.68
0.85
0.92

5%
5%
5%

Electric

Econ
Mid
Lux

13,250.00
27,072.75
42,423.52

3127.624372.41
1492.71

0.00
0.00
0.00

0.15
0.15
0.15

0.54
0.76
0.94

10%
10%
10%

Bikes

Econ
Lux

1373.00
4989.00

1859.00
3031.00

0.02
0.05

0.15
0.15

0.72
1.44

1%
1%

Adv Bikes

Adv Econ
Adv Lux1373.00
4989.00

1859.00
3031.00

0.00
0.00

0.15
0.15

0.00
0.00

5%
5%

Note: ‘Econ’ denotes cars with engine sizes smaller or equal to 1400 cc. ‘Mid’ denotes cars with engine sizes larger than 1400 cc and smaller than 2000 cc. ‘Lux’ denotes cars with engine
sizes larger than 2000 cc. N/A indicates that data is not available or the car technology is not widely used in the country.

Sustainability 2018, 10, 1612

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Table A2. The initial parameters assumed for Japan.
Technology

Engine Size

Prices of Cars
(USD/vehicle)

Standard Deviation ofPrice (USD/vehicle)

Fuel Cost(USD/km)

Discount Rate

Energy Use (MJ/vkm)

Learning Rate

Petrol

Econ
Mid
Lux

12,973.05
22,197.47
31,879.83

4044.809597.81
17,485.23

0.064
0.068
0.077

0.150
0.150
0.150

2.052
2.260
2.740

1%
1%
1%

Adv Petrol

Econ
Mid
Lux

12,973.05
22,197.47
31,879.83

4044.809597.81
17,485.23

0.051
0.054
0.061

0.150
0.150
0.150

1.642
1.808
2.192

5%
5%
5%

Diesel

Econ
Mid
Lux

N/A
N/A
N/A

N/A
N/A
N/A

N/A
N/A
N/A

N/A
N/A
N/A

N/A
N/A
N/A

N/A
N/A
N/A

Adv Diesel

Econ
Mid
Lux

N/A
N/A
N/A

N/A
N/A
N/A

N/A
N/A
N/A

N/A
N/A
N/A

N/A
N/A
N/A

N/A
N/A
N/A

CNG

Econ
Mid
Lux

N/A
N/A
27,472.00

N/A
N/A
0.00

N/A
N/A
0.070

N/A
N/A
0.150

N/A
N/A
2.093

N/A
N/A
1%

Hybrid

Econ
Mid
Lux

27,547.72
31,488.10
40,417.93

836.165548.62
17,197.10

0.023
0.028
0.031

0.150
0.150
0.150

0.684
0.848
0.923

5%
5%
5%

Electric

Econ
Mid
Lux

12,448.00
16,841.40
28,407.61

1300.00
2592.98
2647.72

0.000
0.000
0.000

0.150
0.150
0.150

0.540
0.756
0.936

10%
10%
10%

Bikes

Econ
Lux

4516.00
12,357.00

2292.00
4541.00

0.046
0.057

0.150
0.150

0.720
1.440

1%
1%

Adv Bikes

Adv Econ
Adv Lux4516.00
12,357.00

2292.00
4541.00

0.037
0.046

0.150
0.150

0.000
0.000

5%
5%

Note: ‘Econ’ denotes cars with engine sizes smaller or equal to 1400 cc. ‘Mid’ denotes cars with engine sizes larger than 1400 cc and smaller than 2000 cc. ‘Lux’ denotes cars with engine
sizes larger than 2000 cc. N/A indicates that data is not available or the car technology is not widely used in the country.

Sustainability 2018, 10, 1612

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Table A3. The initial parameters assumed for South Korea.
Technology

Engine Size

Prices of Cars
(USD/vehicle)

Standard Deviation ofPrice (USD/vehicle)

Fuel Cost(USD/km)

Discount Rate

Energy Use (MJ/vkm)

Learning Rate

Petrol

Econ
Mid
Lux

17,842.45
19,342.74
38,942.42

30,920.21
5601.90
29,216.44

0.079
0.103
0.139

0.15
0.15
0.15

2.05
2.26
2.74

1%
1%
1%

Adv Petrol

Econ
Mid
Lux

17,842.45
19,342.74
38,942.42

30,920.21
5601.90
29,216.44

0.045
0.059
0.079

0.15
0.15
0.15

1.64
1.81
2.19

5%
5%
5%

Diesel

Econ
Mid
Lux

N/A
22,294.36
29,919.85

N/A
10,372.06
15,199.82

N/A
0.078
0.093

N/A
0.15
0.15

N/A
2.12
2.40

N/A
1%
1%

Adv Diesel

Econ
Mid
Lux

N/A
22,294.36
29,919.85

N/A
10,372.06
15,199.82

N/A
0.042
0.050

N/A
0.15
0.15

N/A
1.70
1.92

N/A
5%
5%

CNG

Econ
Mid
Lux

N/A
N/A
N/A

N/A
N/A
N/A

N/A
N/A
N/A

N/A
N/A
N/A

N/A
N/A
N/A

N/A
N/A
N/A

Hybrid

Econ
Mid
Lux

23,080.00
30,655.62
35,031.11

1000.00
3111.79
10,048.45

0.040
0.052
0.103

0.15
0.15
0.15

0.68
0.85
0.92

5%
5%
5%

Electric

Econ
Mid
Lux

10,455.00
12,272.00
30,460.00

1000.00
100.001000.00

0.004
0.005
0.008

0.15
0.15
0.15

0.67
0.81
0.93

10%
10%
10%

Bikes

Econ
Lux

2071.00
6306.00

831.002366.00

0.026
0.032

0.15
0.15

0.72
1.44

1%
1%

Adv Bikes

Adv Econ
Adv Lux

2071.00
6306.00

831.002366.00

0.026
0.032

0.15
0.15

0.00
0.00

5%
5%

Note: ‘Econ’ denotes cars with engine sizes smaller or equal to 1400 cc. ‘Mid’ denotes cars with engine sizes larger than 1400 cc and smaller than 2000 cc. ‘Lux’ denotes cars with engine
sizes larger than 2000 cc. N/A indicates that data is not available or the car technology is not widely used in the country.

Sustainability 2018, 10, 1612

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Table A4. The initial parameters assumed for Taiwan.
Technology

Engine Size

Prices of Cars
(USD/vehicle)

Standard Deviation ofPrice (USD/vehicle)

Fuel Cost(USD/km)

Discount Rate

Energy Use (MJ/vkm)

Learning Rate

Petrol

Econ
Mid
Lux

12,936.18
21,320.53
27,991.15

2872.35
3746.18
15,787.11

0.068
0.069
0.095

0.15
0.15
0.15

1.98
2.02
2.77

0.01
0.01
0.01

Adv Petrol

Econ
Mid
Lux

15,523.41
25,584.64
33,589.37

2872.35
3746.18
15,787.11

0.061
0.062
0.085

0.15
0.15
0.15

1.59
1.61
2.22

0.05
0.05
0.05

Diesel

Econ
Mid
Lux

N/A
N/A
33,589.37

N/A
N/A
15,787.11

N/A
N/A
0.072

N/A
N/A
0.15

N/A
N/A
2.53

N/A
N/A
0.01

Adv Diesel

Econ
Mid
Lux

N/A
N/A
33,589.37

N/A
N/A
15,787.11

N/A
N/A
0.065

N/A
N/A
0.15

N/A
N/A
2.02

N/A
N/A
0.05

CNG

Econ
Mid
Lux

N/A
N/A
N/A

N/A
N/A
N/A

N/A
N/A
N/A

N/A
N/A
N/A

N/A
N/A
N/A

N/A
N/A
N/A

Hybrid

Econ
Mid
Lux

19,513.46
22,734.51
45,303.10

2913.994844.60
13,194.43

0.046
0.057
0.068

0.15
0.15
0.15

1.34
1.67
2.00

0.05
0.05
0.05

Electric

Econ
Mid
Lux

18,984.94
31,287.74
40,650.00

190.131529.992080.00

0.000
0.000
0.000

0.15
0.15
0.15

0.21
0.54
0.58

0.1
0.1
0.1

Bikes

Econ
Lux

4516.00
12,357.00

2292.00
4541.00

0.025
0.026

0.15
0.15

0.53
0.77

0.01
0.01

Adv Bikes

Adv Econ
Adv Lux4516.00
12,357.00

2292.00
4541.00

0.000
0.000

0.15
0.15

0.53
0.77

0.05
0.05

Note: ‘Econ’ denotes cars with engine sizes smaller or equal to 1400 cc. ‘Mid’ denotes cars with engine sizes larger than 1400 cc and smaller than 2000 cc. ‘Lux’ denotes cars with engine
sizes larger than 2000 cc. N/A indicates that data is not available or the car technology is not widely used in the country.

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Appendix B. Assumptions for the Policy Scenarios
Table A5. Assumptions for policy scenarios for China.
China

Vehicle Tax(USD)

EV Subsidies(USD)

Registration
Tax (USD)

Fuel Tax(USD/L)

Carbon Tax($/(gCO2 /km))

Fuel Economy
Standard

Kick-Start
Program

BiofuelMandateBaseline scenario
Scenario 1
Scenario 2
Scenario 3

0
Up to 1200
Up to 1200
Up to 1200

0
Up to 9000
Up to 12,000
Up to 15,000

0
Up to 300
Up to 300
Up to 300

0
1.2
1.2
1.2

0
20
40
40

N/A
N/A
20 km/L
20 km/L

N/A
0.3%
1.5%
3%

N/A
N/A
N/A
20%

Table A6. Assumptions for policy scenarios for Japan.
Japan

Vehicle Tax(USD)

EV Subsidies(USD)

Registration
Tax (USD)

Fuel Tax(USD/L)

Carbon Tax($/(gCO2 /km))

Fuel Economy
Standard

Kick-Start
Program

BiofuelMandateBaseline scenario
Scenario 1
Scenario 2
Scenario 3

0
Up to 1200
Up to 1200
Up to 1200

0
Up to 9,000
Up to 12,000
Up to 15,000

0
Up to 300
Up to 300
Up to 300

0
1.2
1.2
1.2

0
20
40
40

N/A
N/A
20 km/L
20 km/L

N/A
0.3%
1.5%
3%

N/A
N/A
N/A
20%

Table A7. Assumptions for policy scenarios for South Korea.
Korea

Vehicle Tax(USD)

EV Subsidies(USD)

Registration
Tax (USD)

Fuel Tax(USD/L)

Carbon Tax($/(gCO2 /km))

Fuel Economy
standard

Kick-Start
Program

BiofuelMandateBaseline scenario
Scenario 1
Scenario 2
Scenario 3

0
Up to 1200
Up to 1200
Up to 1200

0
Up to 9000
Up to 12,000
Up to 15,000

0
Up to 300
Up to 300
Up to 300

0
1.2
1.2
1.2

0
20
40
40

N/A
N/A
20 m/L
20 m/L

N/A
0.3%
1/5%
3%

N/A
N/A
N/A
20%

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Table A8. Assumptions for policy scenarios for Taiwan.
Taiwan

Vehicle Tax(USD)

EV Subsidies(USD)

Registration
Tax (USD)

Fuel Tax(USD/L)

Carbon Tax($/(gCO2 /km))

Fuel Economy
Standard

Kick-Start
Program

BiofuelMandateBaseline scenario

0

0

0

0

0

N/A

N/A

N/A

Scenario 1

Up to 1200

Up to 9000

Up to 300

1.2

20

N/A

0.3%

N/A

Scenario 2

Up to 1200

Up to 12,000

Up to 300

1.2

40

20 km/L

1.5%

N/A

Scenario 3

Up to 1200

Up to 15,000

Up to 300

1.2

40

20 km/L

3%

20%

Note: The baseline scenario assumes that when there is no policy incentive in place. Scenario 1 assumes that there is an increase in the tax incentives and subsidies, with a small kick-start
program. Scenario 2 assumes that there is a larger kick-start program (than scenario 1) in the presence of a fuel economy standard. Scenario 3 assumes there is a biofuel mandate inaddition to a higher EV subsidy and a larger kick-start program than scenario 2.

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Appendix C. Sensitivity Analysis
In this section, we carry out a sensitivity analysis over most relevant technological parameters of
FTT: Transport, including the discount rate, the learning rate, the γ factor and the fuel prices. These
parameters were chosen because they generate the most changes in emissions and technological shares.
The parameters varied here are as follows
1.
2.
3.
4.

Learning rates for the EVs. We did not vary the learning rate for conventional petrol and diesel
cars because the learning for the mature technologies is insignificant;Consumer discount rates;
All γi values simultaneously; (for all vehicle types);
The fuel prices.

The variation used is between 5% and 20%, depending on the parameters (see Tables A9–A12). The
uncertainty range was chosen based on existing literature (discount rate, learning rate) or variations
that we consider as reasonable (the γi values).
We did the sensitivity analysis for the all scenarios. The reason for doing this is that policies
constrain model evolution direction, and thus one should not expect to find the same response forall scenarios.
It is important to analyse model responses to variations in key parameters, in order to ensure thatthe model is not ‘highly sensitive’ to very specific values for any particular parameter. As a benchmark,if the outcome variation is less than the input variation, we assume here that the model is not ‘highly
sensitive’ to the particular values chosen.
Tables A9–A11 show the results for the sensitivity analysis for the four scenarios. The tables show
the change of emissions or market shares (in %) as a result of change in the values of the parameters.
We conclude with this analysis the following broad findings. (1) The effect of learning rates,
discount rates, γi values and fuel prices varies between countries and scenarios. (2) Learning rates,
discount rates and fuel prices have a relatively larger impact on results (changes in outcomes muchlarger than changes in the parameters) than γi values. Overall, the changes in market shares andemissions as a result of the variation in parameter are within 20%.

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Table A9. Sensitivity analysis on key technological parameters in the baseline scenario.
EmissionsTechnology Shares

Country

Variations in Key Parameters

CO2

Petrol Car

Diesel Car

Hybrid

CNG

EV

Motorcycles

China

Learning rate + 5%
Learning rate − 5%
Discount rate + 10%
Discount rate − 10%
All Gamma values + 20%
All Gamma values − 20%
Fuel price + 20%
Fuel price − 20%−2.10%
3.90%
1.80%−3.74%
2.90%
1.40%−5.90%
3.78%−2.48%
6.10%
1.38%−2.35%
2.00%
1.20%−6.61%
4.98%−1.04%
0.12%
0.00%−0.82%
0.00%
0.00%−0.01%
0.02%

1.20%
0.00%
−1.04%
8.13%
0.10%
0.00%
3.07%
0.80%

0.00%
0.00%
2.45%
-8.69%−1.40%−0.85%
3.40%−4.95%

2.32%−6.22%−2.80%
3.74%−0.70%−0.35%−0.55%−0.60%

0.00%
0.00%
0.00%
−0.34%
0.00%
0.00%
0.70%
−0.25%

Japan

Learning rate + 5%
Learning rate − 5%
Discount rate + 10%
Discount rate − 10%
All Gamma values + 20%
All Gamma values − 20%
Fuel price + 20%
Fuel price − 20%−3.45%
0.95%
4.52%−3.56%−2.22%
0.02%−3.22%
3.31%−4.67%
2.00%
2.90%−3.10%−2.37%
0.01%−2.03%
3.77%

0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%

3.57%
−0.45%−2.40%
2.10%
2.39%−0.02%
2.03%−3.55%

0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%

1.10%
−1.55%−0.50%
1.00%−0.02%
0.00%
0.00%−0.22%

0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%

Korea

Learning rate + 5%
Learning rate − 5%
Discount rate + 10%
Discount rate − 10%
All Gamma values + 20%
All Gamma values − 20%
Fuel price + 20%
Fuel price − 20%−4.52%
2.34%
4.80%−3.20%
0.02%
0.05%−0.31%
0.22%−5.60%
2.76%
3.60%
2.92%
0.00%
0.00%
−0.11%
0.12%

0.00%−0.30%−2.00%
0.00%
0.00%−0.01%
0.00%
0.00%

4.64%
−2.27%−1.70%−3.74%
0.01%
0.00%
0.00%
0.00%

0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%

0.97%
−0.19%−0.50%
0.82%
0.00%
0.00%
−0.06%−0.06%

0.00%
0.00%
0.60%
0.00%
0.00%
0.01%
0.17%
−0.06%

TaiwanLearning rate + 5%
Learning rate − 5%
Discount rate + 10%
Discount rate − 10%
All Gamma values + 20%
All Gamma values − 20%
Fuel price + 20%
Fuel price − 20%−0.02%
0.01%
1.20%−0.49%
0.00%−0.57%−5.21%
8.54%−0.01%
0.00%−1.10%−0.41%−0.25%
0.07%−6.00%
4.17%

0.00%
0.00%
0.00%
0.55%
0.00%
0.07%
0.00%
0.00%

0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.54%
1.21%

0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%

0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
1.76%
−1.61%

0.00%
0.00%
1.10%
−0.14%
0.25%−0.14%
3.70%−3.77%

Note: Each number refers to a percentage change in CO2 emissions or technological shares (share of total fleet). Variations used are considered as realistic uncertainty values. Changes inrates are percentage point changes. Outcome changes on emissions are cumulated to 2050, while for shares the values are in 2050.

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Table A10. Sensitivity analysis on key technological parameters in Scenario 1.

Country

Variations in Key Parameters

EmissionsTechnology Shares

CO2Petrol Car

Diesel Car

Hybrid

CNG

EV

Motorcycles

China

Learning rate + 5%
Learning rate − 5%
Discount rate + 10%
Discount rate − 10%
All Gamma values + 20%
All Gamma values − 20%
Fuel price + 20%
Fuel price − 20%−1.64%
1.54%
3.98%−4.10%
1.85%
1.10%−1.74%
1.98%−3.16%
2.74%
2.15%−2.08%
1.88%
1.35%−1.50%
1.83%

0.00%
0.00%
0.01%
−1.00%
0.00%
0.00%
0.00%
0.00%

0.16%
0.00%
−1.62%
1.74%
0.09%
0.00%
0.02%
−0.02%

0.00%
0.00%
3.81%
−2.84%−1.32%−0.95%
0.00%
0.00%

2.99%
−2.73%−4.35%
4.55%−0.68%−0.47%
1.06%−1.65%

0.00%
0.00%
0.00%
−0.37%
0.02%
0.08%
0.42%
−0.15%

Japan

Learning rate + 5%
Learning rate − 5%
Discount rate + 10%
Discount rate − 10%
All Gamma values + 20%
All Gamma values − 20%
Fuel price + 20%
Fuel price − 20%−3.70%
1.90%
3.80%−1.42%−0.44%
0.55%−5.86%
6.90%−3.99%
1.71%
4.20%−1.66%−0.38%
1.37%−1.26%
7.28%

0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%

3.05%
−0.40%−3.70%
1.90%
2.12%
0.00%
7.00%
−4.20%

0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%

0.94%
−1.31%−0.50%−0.24%−1.74%−1.37%−5.74%−3.08%

0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%

Korea

Learning rate + 5%
Learning rate − 5%
Discount rate + 10%
Discount rate − 10%
All Gamma values + 20%
All Gamma values − 20%
Fuel price + 20%
Fuel price − 20%−1.85%
3.61%
7.45%−4.33%
1.33%
0.39%−10.50%12.45%−8.06%
3.35%
6.42%−3.15%
1.64%
0.24%−9.17%
11.50%−0.21%
0.02%
0.41%−1.96%
0.00%
0.00%
4.00%
−5.02%

6.53%−2.65%−3.70%
5.58%
0.08%−2.45%−1.38%
1.73%

0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%

1.83%
−0.72%−3.64%−0.27%−1.73%
2.21%
6.29%−8.12%

0.00%
0.00%
0.51%
−0.20%
0.01%
0.00%
0.26%
−0.09%

TaiwanLearning rate + 5%
Learning rate − 5%
Discount rate + 10%
Discount rate − 10%
All Gamma values + 20%
All Gamma values − 20%
Fuel price + 20%
Fuel price − 20%−0.02%
0.40%
1.60%−0.54%−0.34%−0.05%−7.98%
6.65%−0.01%
0.10%
0.16%−0.47%−0.27%−0.04%−8.53%
0.67%

0.00%
0.00%
0.00%
0.00%
0.26%
0.14%
−3.31%
0.00%

0.00%
0.00%
−0.37%
2.22%
0.43%−0.14%
#
0.13%

0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%

0.14%
−0.22%−0.86%−1.61%−0.48%
0.07%
3.21%−4.80%−0.13%
0.12%
1.07%−0.14%
0.06%−0.03%
5.22%
4.00%

Note: Each number refers to a percentage change in CO2 emissions or technological shares (share of total fleet). Variations used are considered as realistic uncertainty values. Changes inrates are percentage point changes. Outcome changes on emissions are cumulated to 2050, while for shares the values are in 2050.

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Table A11. Sensitivity analysis on key technological parameters in Scenario 2.

Country

Variations in Key Parameters

EmissionsTechnology Shares

CO2Petrol Car

Diesel Car

Hybrid

CNG

EV

Motorcycles

China

Learning rate + 5%
Learning rate − 5%
Discount rate + 10%
Discount rate − 10%
All Gamma values + 20%
All Gamma values − 20%
Fuel price + 20%
Fuel price − 20%−4.80%
3.41%
3.10%−4.77%
1.60%
2.04%−8.10%
4.90%−3.74%
2.63%
3.20%−5.35%
1.11%
0.91%−7.80%
3.21%

0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.01%
−0.01%

0.00%
0.00%
0.04%
0.00%
2.26%
0.00%
2.10%
−2.60%

0.00%
0.00%
0.00%
0.00%
−2.24%−0.67%−2.00%
1.33%

3.74%−2.63%−3.65%
5.09%−1.35%−0.24%
7.32%−1.80%

0.00%
0.00%
0.41%
−0.42%
0.22%
0.00%
0.37%
−0.13%

Japan

Learning rate + 5%
Learning rate − 5%
Discount rate + 10%
Discount rate − 10%
All Gamma values + 20%
All Gamma values − 20%
Fuel price + 20%
Fuel price − 20%−2.45%
4.24%
5.21%−3.41%−0.32%−1.40%−4.50%
5.96%−5.65%
3.42%
3.43%−4.27%−0.37%−0.92%−4.05%
3.83%

0.00%
0.02%
0.00%
0.00%
0.00%
0.00%
0.10%
0.09%

4.32%
−2.75%−4.21%
6.10%
1.79%−0.25%
6.10%
2.11%

0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%

1.33%
−0.69%
0.77%−1.83%−1.42%
1.17%−2.15%−6.03%

0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%

Korea

Learning rate + 5%
Learning rate − 5%
Discount rate + 10%
Discount rate − 10%
All Gamma values + 20%
All Gamma values − 20%
Fuel price + 20%
Fuel price − 20%−2.91%
4.28%
6.45%−4.96%
2.85%
2.80%−5.79%
12.41%−4.12%
1.86%
4.29%−4.19%
2.16%
0.32%-7.00%
11.34%

0.12%
0.01%
0.98%
−0.63%
0.00%
0.00%
3.00%
0.01%

2.10%
−0.23%
0.35%
6.76%
0.11%
−3.24%−1.24%−3.96%

0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%

1.30%
−1.29%−5.61%−0.70%−2.29%
2.92%
4.88%-7.26%

0.60%−0.35%
0.00%−1.24%
0.02%
0.00%
0.36%
−0.13%

TaiwanLearning rate + 5%
Learning rate − 5%
Discount rate + 10%
Discount rate − 10%
All Gamma values + 20%
All Gamma values − 20%
Fuel price + 20%
Fuel price − 20%−3.04%
2.86%
3.14%−2.53%−0.34%−0.27%−6.55%
9.21%−2.85%
2.69%
0.27%−0.83%−0.28%−0.05%−8.44%
7.34%

0.00%
0.00%
0.00%
0.00%
0.29%
0.16%
0.00%
0.00%

0.00%
0.00%
0.67%
3.87%
0.48%
−0.16%
4.33%−3.77%

0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%

3.08%
−2.90%−1.50%−2.81%−0.54%
0.07%
4.11%−3.58%−0.23%
0.21%
0.56%−0.24%
0.05%−0.03%
4.38%
3.36%

Note: Each number refers to a percentage change in CO2 emissions or technological shares (share of total fleet). Variations used are considered as realistic uncertainty values. Changes inrates are percentage point changes. Outcome changes on emissions are cumulated to 2050, while for shares the values are in 2050.

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Table A12. Sensitivity analysis on key technological parameters in Scenario 3.

Country

Variations in Key Parameters

EmissionsTechnology Shares

CO2Petrol Car

Diesel Car

Hybrid

CNG

EV

Motorcycles

China

Learning rate + 5%
Learning rate − 5%
Discount rate + 10%
Discount rate − 10%
All Gamma values + 20%
All Gamma values − 20%
Fuel price + 20%
Fuel price − 20%−6.24%
4.43%
4.10%-8.14%
0.91%
0.81%−9.11%
5.09%−6.58%
4.41%
3.75%−4.69%
0.74%
0.70%−10.92%
2.75%

0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.01%
−0.01%

0.00%
0.00%
0.05%
0.00%
0.03%
0.03%
1.17%
−2.07%

0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
−2.80%
1.06%

6.59%−4.41%−3.80%
5.01%−0.88%−0.73%
12.50%−1.73%

0.00%
0.00%
0.00%
0.00%
0.11%
0%
0.00%
0.00%

Japan

Learning rate + 5%
Learning rate − 5%
Discount rate + 10%
Discount rate − 10%
All Gamma values + 20%
All Gamma values − 20%
Fuel price + 20%
Fuel price − 20%−4.09%
7.12%
6.47%−4.02%−0.99%−0.26%−5.34%
4.58%−6.85%
2.52%
4.12%−5.10%−0.21%−0.16%−5.36%
3.41%

0.00%
0.02%
0.00%
0.00%
0.00%
0.00%
0.00%
0.08%

3.87%
−1.75%−5.05%
0.20%
0.93%
0.45%
−1.45%
1.88%

0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%

2.98%
−0.78%
0.93%
4.90%−0.72%−0.29%
6.8100%−5.37%

0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%

Korea

Learning rate + 5%
Learning rate − 5%
Discount rate +10%
Discount rate − 10%
All Gamma values + 20%
All Gamma values − 20%
Fuel price + 20%
Fuel price − 20%−3.74%
3.66%
3.43%−4.65%−0.05%
0.34%−8.57%
15.22%−2.03%
2.46%
4.13%−3.33%−0.03%
0.04%-10.29%17.92%

0.18%
0.02%
0.94%
−0.50%
0.24%−0.29%
4.41%
0.02%

0.73%
0.25%
−0.02%
5.37%
0.59%−0.97%−1.29%−6.25%

0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%

2.66%
−2.73%−5.41%−1.41%−0.80%
0.98%
6.72%−11.48%

0.00%
0.00%
0.00%
0.00%
0.00%
0.25%
0.00%
0.00%

TaiwanLearning rate + 5%
Learning rate − 5%
Discount rate + 10%
Discount rate − 10%
All Gamma values + 20%
All Gamma values − 20%
Fuel price + 20%
Fuel price − 20%−5.20%
3.40%
1.90%−2.54%−1.65%−0.67%−4.87%
6.07%−2.80%
2.83%
0.59%−1.34%−0.52%−0.39%−5.45%
5.72%

0.00%
0.00%
0.00%
0.00%
0.59%
0.61%
0.00%
0.00%

0.00%
0.00%
0.94%
6.28%
0.89%
0.88%
0.51%
−2.93%

0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%

5.00%
−4.63%−1.53%−3.97%−1.01%−1.06%
4.39%−2.46%−2.20%
1.80%
0.00%
0.00%
0.00%
0.00%
0.54%
−0.33%

Note: Each number refers to a percentage change in CO2 emissions or technological shares (share of total fleet). Variations used are considered as realistic uncertainty values. Changes inrates are percentage point changes. Outcome changes on emissions are cumulated to 2050, while for shares the values are in 2050.

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