2023/5/12 11:11
Insights from adding transportation sector detail into an economy-wide model: The case of the ADAGE CGE model - ScienceDirect
Energy Economics
Available online 8 May 2023, 106710
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Insights from adding transportation sector detail into an economy-wide
model: The case of the ADAGE CGE model
Yongxia Cai a
Michael Shelby
, Jared Woollacott a
, Robert H. Beach a
, Lauren E. Rafelski b
, Christopher Ramig b
,
b
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https://doi.org/10.1016/j.eneco.2023.106710 ↗
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Highlights
•
Transportation detail in economy-wide models significantly alters simulated
outcomes.
•
Electric vehicle (EV) adoption is sensitive to oil price modeling simulations.
•
EV adoption may reduce greenhouse gas (GHG) emissions, particularly with grid
decarbonization.
Abstract
Computable general equilibrium (CGE) models provide valuable insights into economy-wide impacts of anticipated
future structural changes in the transportation sector, yet few CGE models offer detailed transportation
representations. We use an enhanced Applied Dynamic Analysis of the Global Economy (ADAGE) CGE model to
incorporate disaggregated transportation modes and technologies in on-road passenger and freight transportation.
We assess the impacts of these inclusions on U.S. transportation patterns, energy consumption and greenhouse gas
emissions. Simulating illustrative global oil price cases with and without transportation detail, we find subsector
mode disaggregation and technology additions in a CGE model significantly alter the impacts of oil prices on global
trade and freight patterns, energy consumption, and greenhouse gas (GHG) emissions. We find that: (1) alternative
technologies are essential for capturing transportation sector impacts, (2) electrification may reduce emissions with
electricity decarbonization, and (3) higher oil prices may hasten electrification.
Keywords
Alternative-fuel vehicles; Economic modeling; Oil price; Transportation policy
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1. Introduction
Transportation is an important sector for global economic modeling of energy systems, accounting for 29% of global
final energy consumption in 2018 (International Energy Agency, 2020). However, this sector has generally received
less attention in economy-wide models than some other major parts of the energy system, particularly the
electricity sector (Babatunde et al., 2017). Key challenges in modeling transportation include sectoral heterogeneity,
technical complexity, representation of consumer travel behavior, and data availability (Tavasszy et al., 2011; Yeh et
al., 2017; Zhang et al., 2018). In addition, previous literature has suggested the transportation sector offers relatively
less potential for substantial changes in technology than other components of the energy system, citing limited
cost-competitive fuels (Mathiesen et al., 2015). However, recent advancements in vehicle technology, especially
electric vehicles (EVs), provide motivation to update assessments of the transportation sector's potential for
substantial shifts in emissions and energy consumption.
Passenger and freight transportation services typically rely on long-lived vehicles with a variety of important
physical attributes, including, but not limited to, energy type, energy efficiency, range, capacity, and lifetime. Each
of these vehicle attributes requires some level of definition in transportation modeling to accurately simulate
energy, emissions, and economic outcomes. The consumer value of vehicles is determined by their age and their
energy requirements, performance characteristics (e.g., power/torque), and other amenities (e.g., comfort/safety)
(Greene et al., 2018). Cost and consumer utility trade-offs associated with vehicle attributes (e.g., between capital
cost and vehicle range) can be challenging to analyze when modeling vehicle consumer choices.
Taken together, transportation system complexities can quickly outstrip data availability and model computational
capabilities and therefore generally require a significant amount of simplification. Modelers looking to represent
additional transportation sectoral, technological, and spatial detail as part of a larger modeled economic system
face trade-offs under constraints from numerical solution algorithms and computing power limitations.
Computable general equilibrium (CGE) models typically use standard economic data from national income accounts
and offer a rich set of endogenous economy-wide feedback, but they generally do so at the expense of subsectoral
detail and specific individual technology characteristics, and most CGE models are constrained in the amount of
technological detail they can incorporate. Thus, balancing the trade-offs of adding in the technological and spatial
details of transportation sector-only models while gaining the insights from an economy-wide CGE perspective
requires some degree of modelers' judgment.
Despite these challenges, enhancing representation of the transportation sector within CGE models may improve
understanding of potential economy-wide impacts of future shifts in energy and transportation service
consumption. Prior work (Wade et al., 2019; Zhang et al., 2018; Britz and van der Mensbrugghe, 2016) has shown
that aggregation of technological and spatial detail in economic models can mask important sources of
heterogeneity, potentially leading to substantially different predicted outcomes between models employing
different levels of aggregation. In CGE models, this is sometimes labeled “aggregation bias.” For example, Cai and
Arora (2015) explored the importance of disaggregating electricity generation in a CGE model when assessing the
impacts of environmental policy, finding that incorporation of heterogeneous generation technologies has
important effects on the estimated cost of mitigation.
Within the transportation sector, there are opportunities for passenger and freight mobility demands to substitute
among modes (e.g., between freight services provided by truck, rail, and marine shipping) and energy technologies
(e.g., petroleum, battery electric, hydrogen fuel cell electric). Aggregating these modes and representing only
conventional technologies, as many CGE models do, prevent substitution, either by fixing market shares for modes
and/or technologies or by allowing only for exogenous changes in market shares. This reduced flexibility limits
options for modeling how the economy adjusts to economic and policy shocks or long-term developments in the
transportation sector. Even if technologies' cost structures are expected to remain constant over time, modeled
shifts in resource costs or other exogenous macroeconomic shocks can still affect technologies' competitiveness.
Models with single-technology and aggregated-mode representations that do not allow for endogenous
substitution among alternative technologies and modes of transport are likely to find different economic outcomes
than models that allow such competition through more detailed representation.
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To study the potential significance of more detailed transportation technology and sectoral representation in
economy-wide modeling, we disaggregated transportation sector modes in the Applied Dynamic Analysis of the
Global Economy (ADAGE) global CGE model and incorporated technologies representing multiple types of
alternative-fuel vehicles (AFVs). We then compared results between two versions of the model that incorporate
disaggregated transportation modes, both with and without the addition of AFVs. Incorporating AFV technologies
(version 1) has a substantially larger impact on energy and emissions results than disaggregating transportation
modes alone (version 2); therefore, we focus on comparing model results with and without AFV technologies in the
results and discussion sections (see Fig. A1 in Appendix A).
The scenarios presented in this study are based on illustrative oil price trajectories, a key driver of transportation
service costs for traditional vehicles with internal combustion engines. We found including new vehicle technology
options for on-road transportation modes in ADAGE leads to differences in the modeled mix of transportation
energy demand, energy intensity of transportation services, and total greenhouse gas (GHG) emissions. Specifically,
we found that (1) on-road vehicle electrification can significantly reduce emissions with concomitant electricity
sector decarbonization, (2) higher (or lower) oil prices can hasten (or slow) the transition to EVs with hybrid
vehicles moderating the transition, and (3) representation of AFV technologies is essential for capturing these
changes. These differences illustrate the economic and environmental significance of sectoral technology
representation in economy-wide models. These findings may inform future studies of modeled technology
competition and environmental impact assessments of the transportation sector.
2. Literature review
The global transportation sector is a large and growing user of energy and a major source of associated emissions
(Intergovernmental Panel on Climate Change, Edenhofer, Pichs-Madruga et al., 2014) and health impacts (Anenberg
et al., 2019). Thus, transport is a vital component of energy systems and energy-environment modeling and is
typically represented in detail within energy system models. Energy demands from transportation may shift
through several pathways, including changes in transport demand, shifts in demand among the different modes of
travel, reductions in energy intensity of vehicle technologies, and adoption of new vehicle technologies and fuel
types (Creutzig, 2015). Transportation-only sector modeling studies often analyze specific pathways and
components of this system (e.g., a single-fuel production pathway, a single-sector technology adoption, specific
refueling infrastructure) without capturing key market interactions important for understanding the implications
for the overall energy system (Muratori et al., 2020). Although the number of studies assessing global transport has
increased in recent years, they have tended to limit the analysis to the light-duty vehicle (LDV) sector (Meyer et al.,
2007; Grahn et al., 2009; Kyle and Kim, 2011; Karplus et al., 2013a; Bosetti and Longden, 2013; Hao et al., 2016;
Edelenbosch et al., 2017; Green et al., 2019).
Several energy system models represent the full transportation sector, but many of these models lack full
integration with the rest of the economy (Grahn et al., 2013). Capturing the whole economy is especially useful both
for understanding freight demand, which is closely tied to gross domestic product (GDP), and when considering the
potential for transport electrification, because increasing the use of EVs also increases electric power demand. The
mix of generation technologies (e.g., coal- or natural gas-fired electricity/renewable sources of power such as wind
and solar) used to meet an increase in electricity demand could significantly influence other markets (e.g., capital,
energy) and overall energy and emissions outcomes.
Energy-focused, economy-wide models have frequently been used to capture costs and energy use over broad
spatial (e.g., country/global) and temporal scales (e.g., mid-century and/or end-of-century time frames), including
interactions across markets. As previously noted, given the importance of transportation for energy demand in the
United States and globally, most energy-focused economy-wide models include transportation modules. Some of
the models that fall under this category are integrated assessment models such as the Global Change Analysis
Model (GCAM) (Pacific Northwest National Laboratory, 2021), Model for Energy Supply Systems and Their General
Environmental Impact (Huppmann et al., 2019; “MESSAGEix model & framework”, MESSAGE), Prospective Outlook
on Long-term Energy System (POLES) (Després et al., 2018), and Regional Model of Investments and Development
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(REMIND) (Luderer et al., 2015); energy-economy-environment-engineering models such as CIMS (Jaccard, 2009),
Energy System Modeling Environment (“ESME”) (Heaton, 2014), National Energy Modeling System (NEMS) (Nalley
et al., 2019), and The Integrated MARKAL-EFOM System (TIMES) (Loulou et al., 2005); and CGE models such as AsiaPacific Integrated Model (AIM) (Akimoto et al., 2015), Economic Projection and Policy Analysis (EPPA; Paltsev et al.,
2005), General Equilibrium Model for Economy-Energy-Environment (GEM-E3) (Capros et al., 2013), and IMACLIMR (Sassi et al., 2010; Waisman et al., 2012).1
Although these energy-focused economy-wide models may be well suited for modeling long-term impacts of
alternative technology, policy, and market outcomes, most models characterize transportation at a relatively
aggregate level, particularly CGE models. Aggregating, or averaging, across scales is generally necessary for
representing physical and economic parameters at the level of sectoral, regional, and production technology detail
used within these individual models. Aggregation is also important for aligning data available at different scales and
making models computationally tractable.
However, there are important trade-offs, including potential bias, that can result from combining and averaging
across heterogeneous input data. Multiple studies have identified potential issues related to aggregation bias, which
refers to the possibility that model results will systematically differ as a function of the level of aggregation (Britz
and van der Mensbrugghe, 2016; Caron, 2012; Himics et al., 2020; Horridge, 2018; Wade et al., 2019; Zhang et al.,
2018). The literature suggests that certain key model results, such as trade and welfare changes, depend on the
aggregation level selected (Ko and Britz, 2013), and significant differences may exist at the subsectoral level
(Alexeeva-Talebi et al., 2012; Zhang et al., 2018). Brockmeier and Bektasoglu (2014) conducted a sensitivity analysis
using different aggregations on general equilibrium (GE) and partial equilibrium (PE) versions of the Global Trade
Analysis Project (GTAP) model and found that data aggregation, especially sectoral aggregation, has a much larger
influence on results than model structure from GE and PE. Although the potential for model results to differ based
on level of aggregation has been recognized, relatively few studies have attempted to quantify the magnitude of
these impacts (Britz and van der Mensbrugghe, 2016). Transportation inherently involves movement of people and
goods across space and thus may be impacted by the level of spatial disaggregation modeled and assumptions
regarding the movement of people, goods, and services across regions.
Several studies have conducted comparisons across multiple global transportation models, exploring projections
under baseline and various emissions mitigation scenarios (Edelenbosch et al., 2017; Girod et al., 2013; Pietzcker et
al., 2014; Yeh et al., 2017). The models compared in these studies typically project a large increase in demand for
transportation services over the next few decades with technological change having a major influence on future
energy consumption and emissions. Typically, these models show substantially reduced transportation energy use
and emissions in their projections only when they incorporate advanced technologies that are less energy intensive
or GHG intensive than incumbent technologies. In general, there is considerable uncertainty regarding model
baseline projections, as well as future changes in transportation energy intensity and emissions under alternative
scenarios. Key drivers of uncertainty include global demand for transportation, vehicle capital cost projections, fuel
prices, nonprice factors affecting adoption of new technologies, potential for modal shifts, and the availability and
influence of transportation infrastructure. These studies include relatively few CGE models, although the AIM/CGE,
GEM-E3, and IMACLIM-R models have been used in these types of comparisons. The EPPA model is another CGE
model that has been widely used in transportation-focused studies (Paltsev et al., 2004; Schäfer and Jacoby, 2005;
Karplus et al., 2013b; Karplus et al., 2010; Green et al., 2019).
As the costs of AFVs, such as EVs, have come down, modeling scenarios have placed greater emphasis on the energy
impacts and environmental benefits they offer. As a result, EV technologies have taken an increasingly prominent
role in projections of the future transportation sector. Still, most CGE models generally provide limited
representations of alternative-fuel technologies, although notable exceptions include the EPPA (Karplus et al., 2010;
Karplus et al., 2013b) and GEM-E3 (Karkatsoulis et al., 2017) models. For example, EPPA disaggregates household
transportation from the overall transportation sector and introduces battery and plug-in hybrid vehicles to compete
with conventional vehicles within the model. Table 1 summarizes the model structural attributes' sectoral detail for
transportation and electricity for a set of identified energy-focused economy-wide CGE models. Although
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technology details for the electricity sector and transportation sector modal disaggregation are common, relatively
few models have rich advanced technology details in their transportation sectors.
Table 1. Economy-wide CGE models' transportation attributes.
Model
Institution Model
Dynamics
Country
Time
Capital
Coverage
Frame
Vintaging
Transportation
Modes
Power
Advanced
Refere
Technology
Technology
ADAGE
RTI
Recursive-
Global/8
5-year
dynamic
regions
Yes
Light duty,
Hybrid,
interval
freight truck,
natural gas, technologies: (2023)
2010–
bus, air, water, battery,
coal, oil, gas,
2050
rail freight,
combined
fuel cell,
9
rail passenger, hydrogen
cycle, hydro,
and rest
nuclear,
for all on-
Cai et a
road modes solar, wind,
geothermal
AIM/CGE
NIES
Recursive-
Global/17
1-year
Yes
Car, bus, rail,
N/A
12
Fujimo
dynamic
regions
interval
domestic air,
technologies: (2017)
2005–
international
coal, oil, gas,
2100
air, two-
nuclear,
wheelers
hydro,
geothermal,
photovoltaic
(P)V, onshore
wind,
offshore
wind, waste,
other
renewables,
advanced
biomass
Dart
EC-MSMR
KIEL
Recursive-
Global/20
1-year
Institute
dynamic
regions
Grouped with N/A
Coal, natural
Jin et a
interval
other in the
gas, hydro,
(2019)
for World
2011–
mobility
nuclear
Economy
2030
sector
ENV
Recursive-
Global/16
5-year
Canada
dynamic
regions
interval
Yes
Yes
Air and other
transport
N/A
11
Zhu et
technologies: (2018)
2010–
fossil fuel
2050
based,
nuclear,
hydro, wind,
solar,
geothermal,
biomass,
coalintegrated
gasification
with carbon
capture,
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Model
Insights from adding transportation sector detail into an economy-wide model: The case of the ADAGE CGE model - ScienceDirect
Institution Model
Dynamics
Country
Time
Capital
Coverage
Frame
Vintaging
Transportation
Modes
Power
Advanced
Refere
Technology
Technology
natural gas
power
generation
with carbon
capture,
biomass
power
generation
with carbon
capture, solar
power
generating
with storage
ENGAGE
UCL
Recursive-
Global/17
2007–
dynamic
regions
2030
No
Air, water,
N/A
1 sector
other
Winni
al. (201
transport
ENVISAGE
GTAP
Recursive-
Global/
Flexible Yes
Air, water,
dynamic
flexible with
step
other
Mensb
transport
(2018)
20–30 regions
ENV-
OECD
Linkages
Recursive-
Global/29
Extends Yes
dynamic
regions
to 2050
1 sector
N/A
N/A
1 sector
7
van de
Châtea
technologies: (2014)
fossil fuel
based, hydro
and
geothermal,
nuclear, solar
and wind,
and
renewable
combustibles
(including
biomass) and
waste, gas
carbon
capture and
sequestration
(CCS), coal
CCS
EPPA
MIT/US
Global/18
5-year
EPA
regions
interval
Yes
LDVs, other
LDV
18
“EPPA
transport
includes
technologies: Structu
2010–
ICE, battery coal, natural
(MIT Jo
2100
electric,
gas, oil,
Progra
hydrogen,
advanced
the Sci
plug-in
gas,
and Po
electric
advanced
Global
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Model
Insights from adding transportation sector detail into an economy-wide model: The case of the ADAGE CGE model - ScienceDirect
Institution Model
Dynamics
Country
Time
Capital
Coverage
Frame
Vintaging
Transportation
Modes
Power
Advanced
Refere
Technology
Technology
coal, coal
Chang
with CCS,
2023)
coal + bio cofiring with
CCS, gas with
CCS, gas with
advanced
CCS, nuclear,
advanced
nuclear,
hydro, solar,
wind, wind
with backup,
solar with
backup,
biomass,
biomass with
CCS
EU-EMS
PBL
Recursive-
Global/67
2010–
dynamic
regions
2050
No
Grouped in
N/A
1 sector
other sectors
(Niam
2020,
Thisse
2019)
EXIOMOD
TNO
CGE
Global/44
2015–
Rail, other
N/A
12
(Ivanov
countries and
2050
land, sea and
technologies: 2019)
a Rest of
coastal water,
coal, gas,
Bulavs
World
inland water,
nuclear,
al. (201
air, rest
hydro, wind,
oil, biomass
and waste,
solar PV,
solar
thermal,
wind,
geothermal,
other
FARM
USDA
Recursive-
Global/13
2004–
Air, water,
dynamic
regions
2050
other
N/A
9
(Sands
technologies
Sands
with one for
2014)
bioelectricity
using
switchgrass
Gdyn
GTAP
Dynamic
Global/12
regions
https://www.sciencedirect.com/science/article/pii/S0140988323002086#f0005
No
Land, air,
water
N/A
1 sector
Antim
al. (201
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Insights from adding transportation sector detail into an economy-wide model: The case of the ADAGE CGE model - ScienceDirect
Institution Model
Dynamics
Country
Time
Capital
Coverage
Frame
Vintaging
Transportation
Modes
Power
Advanced
Refere
Technology
Technology
GEM-E3
JRC
Recursive-
Global/38
5-year
Yes
Air, road
EVs
11
Capros
dynamic
regions
interval
passenger, rail
technologies: (2013)
2011–
passenger,
coal, oil, gas,
2050
water
nuclear,
passenger,
biomass,
road freight,
hydro, wind,
rail freight,
PV,
water freight
geothermal,
CCS coal, CCS
gas
ICES
IGEM
CMCC
Recursive-
Global/25
1-year
dynamic
regions
Insight-
Inter-
works
temporal
No
1 sector
N/A
Coal, gas, oil,
“ICES”
or 5-
nuclear,
tempo
year
hydro, solar,
Compu
interval
wind
Equilib
2011–
System
2050
(ICES,
US
No
Grouped in
N/A
1 sector
the
Goettle
(2009)
transportation
and
warehousing
sector
IMACLIM-R
MAGNET
CIRED
Hybrid
Global/12
1-year
dynamic
regions
interval
Yes
Air, water,
N/A
1 sector
other
(Sassi
2010)
through the
2001–
(Waism
recursive
2100
al., 201
iteration of
“Mode
annual static
concep
equilibria
solver
and dynamic
details
modules
IMACL
recursive-
(UCL W
dynamic
2014)
LEI
Global
No
N/A
Woltje
(2014)
MAGNET
LEI
Recursive-
Global/134
dynamic
regions and
No
Air, water,
N/A
1 sector
other
Philipp
al. (201
countries
MIRAGE-e
CEPII
Recursive-
Global/19
Up to
dynamic
regions
2100
No
2 sectors: car
N/A
and truck,
Coal, oil, gas
Fontag
al. (201
other
MIRAGRODEP IFPRI
Recursive
Global/141
region or
country
https://www.sciencedirect.com/science/article/pii/S0140988323002086#f0005
Yes
Air, water
other
N/A
“The
MIRAG
Model
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Insights from adding transportation sector detail into an economy-wide model: The case of the ADAGE CGE model - ScienceDirect
Institution Model
Dynamics
Country
Time
Capital
Coverage
Frame
Vintaging
Transportation
Modes
Power
Advanced
Refere
Technology
Technology
nation
Policy
Resear
Institu
(IFPRI)
PACE
ZEW
Comparative- Global/50
static
No
regions
Grouped in
N/A
energy
Grouped
Clauss
with heat
Schube
intensive
(2009)
sector
REMIND
SAGE
PIK
EPA
Inter-
Global/11
5-year
temporal
regions
Inter-
U.S./4 regions
temporal
LDVs (internal N/A
14
interval
combustion
technologies: (2015)
2005–
engine
coal, gas, oil,
2100
battery
coal with
electric
CCS, gas with
vehicle (BEV)
CCS,
and fuel cell
bioenergy,
vehicle),
bioenergy
passenger
with CCS,
train, heavy-
geothermal,
duty freight,
nuclear,
passenger-
hydro, solar,
aviation and
solar-central
buses, electric
PV, solar-CSP,
rail
wind
5-year
Yes
Yes
interval
Truck,
No
1 sector
nontruck
Ludere
Marten
(2021)
2016–
2081
SNoW-NO
TEA
Statistics
Recursive-
Norway
dynamic
Norway
1-year
No
Air, water,
interval
public
2013
transport,
beyond
other
PPE/
Recursive-
Global/18
5-year
COPPE
dynamic
regions
interval
No
Air, water,
N/A
1 sector
Lindho
(2019)
N/A
1 sector
other
Cunha
(2020)
2011–
2100
USREP
MIT
Recursive-
U.S./11 or 30
5-year
dynamic
regions
interval
2006–
2100
Yes
Personal and
Battery,
8
USREP
commercial
plug-in
technologies: et al., 2
hybrid
conventional,
biomass,
wind and
solar, wind
with gas
backup, wind
with biomass
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Model
Insights from adding transportation sector detail into an economy-wide model: The case of the ADAGE CGE model - ScienceDirect
Institution Model
Dynamics
Country
Time
Capital
Coverage
Frame
Vintaging
Transportation
Modes
Power
Advanced
Refere
Technology
Technology
backup,
advanced
gas,
advanced gas
with CCS,
advanced
nuclear
WEGDYN_AT
Wegener
Static-
Global/Austria
No
Motorized
Center
comparative
individual
and
transport, five
recursive-
land transport
dynamic
(rail freight,
mode
rail passenger
N/A
Grouped in
“WEGD
other sector
(Sentin
2023)
long-range,
road freight,
short range
public
transport,
other
transport
services) (e.g.,
postal
services,
warehousing),
air, water
3. Methodology
3.1. ADAGE model overview
The ADAGE model is a multiregion, multisector, recursive-dynamic CGE model. ADAGE follows the classical ArrowDebreu GE framework (Arrow and Debreu, 1954) covering all aspects of the economy, including factor endowments,
production, private and public consumption, trade, and investment. ADAGE includes eight regions (Africa, Brazil,
China, European Union 27, United States, Rest of Asia, Rest of South America, and Rest of World) and simulates
regional and global economic activity from 2010 through 2050 in 5-year increments. Intertemporal dynamics in
ADAGE are represented by (1) growth in the available effective labor supply from population growth and changes in
labor productivity; (2) capital accumulation through savings and investment; (3) changes in stocks of natural
resources; and (4) technological change from improvements in energy efficiency, productivity, and advanced
technologies (e.g., AFVs) that become cost competitive in later time periods.
Within each period, representative firms in each sector maximize profits subject to technology constraints also
specified by a constant elasticity of substitution (CES) functional form. This production structure guides the
transformation of factors of production and intermediate inputs into commodities. The model includes more than
60 sectors with distinct representations of fossil fuels, electricity generation technologies, and transportation
modes. One representative household per region allocates its earnings to consumption of these commodities and
investment. The composition of consumption of goods and services is responsive to relative prices and is guided by
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a nested CES function. Households allocate their time to earning income in labor markets and enjoying leisure, and
the relative prices of consumption and labor hours (i.e., wages) guide their allocation. ADAGE represents bilateral
trade flows among regions; households and firms exhibit preferences between consumption goods by origin (i.e.,
domestic versus foreign) via Armington aggregation (Armington, 1969).
ADAGE tracks environmentally important inputs and outputs (e.g., land, fuels, emissions) in physical and monetary
units. The representation of the economy with rich sectoral detail makes ADAGE suitable for analyzing a wide range
of economic and environmental policies and estimating how all parts of an economy respond to those policies
(Clarke et al., 2016; Calvin et al., 2016; Lucena et al., 2018; Kober et al., 2016; van Ruijven et al., 2016). The core
database underlying ADAGE is GTAP version 7.1 (Narayanan and Walmsley, 2008), complemented by numerous
other data sources to extend the physical and technological detail in ADAGE. This study is the first application of a
new version of ADAGE with an enhanced version of the transportation sector. Thus, in this paper we focus on
describing the modifications introduced to the transportation and energy sectors. See (Cai et al., 2023) for full
documentation of the ADAGE model, including detailed discussion of our additions and modifications to the data
available from the GTAP database.
3.1.1. Production in the transportation sector
The transportation sector in ADAGE includes (1) eight modes of transportation that use conventional technologies
(light-duty passenger, passenger buses, heavy-duty freight, rail freight, rail passenger, airline, water transportation,
and other transportation) and (2) four types of AFV technologies (battery electric vehicles [BEVs], hybrid vehicles,
compressed natural gas vehicles, and hydrogen fuel cell EVs) for all on-road transportation. On-road vehicles were
assumed to have a maximum useful lifetime of 30 years in the model, and the average survival rate was lower for
LDVs than for passenger buses and heavy-duty freight. The production of transportation service for each on-road
mode and technology in ADAGE is vintaged: new vehicles were defined as those from age zero years through the end
of age 4, and used vehicles were defined as vehicles age 5 years or above.
Production of new vehicles using conventional and AFV technologies is structured with a series of nested CES
functions using energy, capital, labor, and materials as inputs (see Fig. 1). Energy used in transportation varies by
mode and technology, as shown in Fig. 2. Currently, most finished gasoline in the United States contains up to 10%
ethanol by volume (E10), whereas a smaller amount of biodiesel is blended into petroleum diesel. Overall, biofuels
accounted for about 5% of total U.S. energy consumption in the transportation sector in 2020; ethanol supplied
about 4% and biodiesel and renewable diesel combined supplied about 1% (Energy Information Administration
(EIA), 2019). For this study, first- and second-generation biofuels can be blended with refined oil and replace refined
oil used in all on-road transportation modes. The fuel blending ratio differs for LDVs, freight trucks, and passenger
buses with relatively more ethanol than biodiesel in LDVs and more biodiesel in heavy-duty vehicles. Refined oilbiofuel blends and electricity were used in hybrid vehicles at a fixed ratio to represent the average hybrid vehicle in
each global region. Because hydrogen is not presently modeled as a commodity in ADAGE, we collapsed the
production of hydrogen, which uses electricity and natural gas as inputs, and the production of transportation
service, which uses hydrogen and other inputs, into one composite production function for fuel cell technology.
Natural gas vehicles represent the combination of natural gas–only vehicles and bi-fuel vehicles (gasoline and
natural gas), where natural gas and refined oil cannot substitute with each other in ADAGE. Battery vehicles use
electricity and do not differentiate the electricity source (fossil fuel or renewables). In general, battery vehicles have
the highest energy efficiency, followed by fuel cell vehicles, hybrid, and conventional. Natural gas vehicles have the
lowest fuel efficiency (see Table A1 in Appendix A).
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Fig. 1. New on-road transportation production structure by mode and technology.
This figure depicts the production structure for new on-road transportation vehicles by mode and technology by
displaying its inputs and connecting them with lines to indicate how they are combined in the production function.
Each vertex includes inputs whose substitution is parameterized by the elasticity displayed for that vertex.
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Fig. 2. Energy used in on-road transportation by technology.
This chart shows the included fuel or energy type used in on-road transportation. Conventional includes oil and
biofuels. Battery, hybrid, and fuel cell include electricity. Hybrid and gas include oil-biofuel. Fuel cell and gas include
natural gas.
For each new vehicle technology, the energy mix bundle can substitute with capital to improve energy efficiency.
The resulting energy/capital composite was then combined with materials and labor within a Leontief production
function (i.e., assuming no substitution between these inputs). A fixed factor was included for new vehicle
production with an elasticity of substitution of 0.1 (fuel cell vehicles and natural gas) or 0.2 (battery and hybrid
electric) at the top level of the production function (see Fig. 1 for the new vehicle production structure). The fixed
factor was assumed to be 0.1% of service production value with an equivalent endowment in 2010. As the model
solves for later periods, the endowment is updated to larger or smaller values based on the solution from the
previous period. The approach for defining the fixed factor reflects the fact that retirement of conventional
technology and adoption of AFVs are both determined by more than cost per mile traveled. The fixed factor acts as a
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proxy for the cost and availability of supportive infrastructure, infrastructure network externalities, consumer
preferences regarding vehicle technologies, and other nonpriced factors that might slow AFV adoption beyond what
an input cost-per-service basis would imply. Fixed factor parameterization is commonly accomplished through
modeler judgment (Chen et al., 2022). Moreover, elasticity estimates for nascent technologies may not be reliable
for later periods with higher penetration. The rate of technology penetration is an important sensitivity for model
calibration worthy of further study.
Vehicle technologies in the U.S. region are subject to national fuel efficiency standards. More specifically, we
assumed that the U.S. region transportation sector must meet the Phase 2 fuel efficiency and GHG emissions
standards for both light-duty and heavy-duty vehicles (Environmental Protection Agency et al., 2016; National
Highway Traffic Safety Administration, 2010). For our representation of the real-world fuel economy levels required
to meet these standards, we relied on average fuel economy projections for LDVs, freight trucks, and buses from the
Annual Energy Outlook (AEO) 2018 Reference Oil Price case, which assumes full compliance with these standards
(U.S. Department of Energy, Energy Information Administration (EIA), 2018). These assumed average fuel economy
levels must be met on average across all new vehicles sold, so no one vehicle technology need satisfy the standard
unless there is only one technology. To model the fuel efficiency standards, technologies purchase credits for their
fuel use and produce credits at the rate implied by the efficiency standard. Thus, technologies more efficient than
the standard are subsidized at the expense of less efficient technologies, the latter of which must purchase more
credits than they produce. The model structure and elasticity of substitution are described in the ADAGE
documentation (Cai et al., 2023).
To characterize AFVs in each mode where they are available (e.g., LDVs, heavy-duty vehicles, buses), we used a
bottom-up approach to obtain vehicle purchase costs, expenses for charging stations and other infrastructure,
vehicle insurance, maintenance costs, and fuel economy over time and to see how they change relative to each
vehicle's conventional counterpart. The parameter values used in this work were informed by external data,
including Department of Energy battery pack and fuel cell stack technology, interim and ultimate target fuel
intensity, and costs (U.S. DOE, 2015; Nykvist and Nilsson, 2015; BNEF, 2015), and AEO 2019 data (Energy Information
Administration (EIA), 2019). We reviewed near-term battery pack cost estimates by extrapolating from Nykvist and
Nilsson (2015) to 2020 and then to mid-century using U.S. DOE (2015) 2050 estimates. Our battery pack costs are
therefore drawn from a variety of sources to construct illustrative, forward-looking estimates combined by the
authors' expert judgment. The authors have developed plausible values of future vehicle efficiency and cost for use
in scenarios demonstrating the novel transportation sector methods employed in ADAGE and reported in this paper.
However, these estimates do not constitute a projection of future vehicle costs or efficiencies and should not be
interpreted in such a manner.
AFV purchasing costs were aggregated to capital, labor, and materials inputs in ADAGE. Purchase and infrastructure
costs were amortized into annual capital costs using an assumed 30-year maximum lifetime for all on-road
vehicles. Energy input cost was derived from energy price in the ADAGE base year and fuel economy. The sum of
labor, capital, materials, and energy input costs form the transportation service production cost and vary by region
of the world. Error! Reference source not found.32 provides a comparison of production cost per mile traveled for
all new on-road vehicles in the United States. For conventional vehicles, the cost reduction is induced by fuel
efficiency improvements only, whereas for AFVs, cost declines also stem from technology improvements that
reduce capital, labor, and material costs. We assumed battery, hybrid, and fuel cell vehicle costs decline over time.
One or more of these AFVs become cost competitive with conventional vehicles by 2030, although the trajectories
vary by mode. The current pace of change for AFV costs in these sectors is rapid, and any cost estimates will likely
change in the future. Still, these figures show the types of inputs required to operationalize and demonstrate the
validity of these methods.
The transportation service production cost (e.g., energy, capital, insurance, maintenance, and infrastructure costs)
for new vehicles was updated in each period in the model using data in Fig. 3. The new vehicle's fixed-factor
endowment in each current period was assumed to be proportional to its service production in the previous time
period. The service production cost for used vehicles was guided by the age structure of vehicles added in prior
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periods. In ADAGE, a vintaged capital stock serves a comparable role to the fixed factor in new vehicles, which is not
present for used vehicles. ADAGE transportation service production for used vehicles (aged 5–29 years) for both
conventional and alternative-fuel technologies use the same set of energy, capital, labor, and materials inputs as
when a given vintage of used vehicle was new with no substitution possibilities (i.e., the input mix is determined
when new vehicles are produced, but can no longer be changed once they have been made). Therefore, the used
fleet efficiency can only be improved by the retirement of old vehicles and the adoption of new, more efficient
vehicles. ADAGE's relative proportions of vehicle miles traveled (VMT) being provided by vehicles in each vintage
are determined by the number of new vehicles added in each prior period adjusted for the average VMT driven by
vehicle age. ADAGE's vehicle survival rate combined with a VMT schedule for surviving vehicles by age (in annual
time steps) informs the weighting of surviving vehicle attributes (e.g., fuel efficiency, nonfuel costs) in the vintage
stock, with data coming from the Motor Vehicle Emission Simulator (MOVES) (2014a) model developed by the U.S.
Environmental Protection Agency. Vehicle survival rates and annual VMT decline as vehicles age, reducing the
relative influence of older vehicles on the vintage fleet efficiency. Conventional vehicles and AFVs were assumed to
have the same average VMT schedule.
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Fig. 3. Transportation service costs for all new on-road vehicles in the United States during 2010–2050 ($/VMT).
This line graphs shows US$ per vehicle mile traveled (VMT) for each model year from 2010 to 2050. Lines represent
conventional and electric vehicles and are separated by light duty, freight truck, and bus. Bus has the highest $/VMT
for all vehicle types, followed by freight then light duty. Both conventional and electric vehicles $/VMT drops from
2010 to 2050, with a substantial drop for fuel cell and battery.
3.1.2. Demand for transportation services
Transportation services are demanded by producers, governments, and households. For producers and governments
in ADAGE, transportation services enter a Leontief structure, meaning, for example, that there is no substitution
between on-road freight and rail freight transportation (taken as a simplifying assumption). The ADAGE model uses
a nested CES structure to represent household consumption preferences. In each period, households generate utility
from consumption of leisure and composite goods (see Fig. 4). A composite good, aggregated from housing, food,
manufacturing, transportation, and others, can be traded against leisure time to obtain a specific utility for each
household in each period. The elasticity of substitution between the composite good and leisure (σcl) represents
how willing households are to trade off leisure time for consumption (i.e., sacrifice leisure time to earn a wage and
purchase more consumption goods and services). Within the composite good, transportation is substitutable with
an other goods aggregate consisting of a combination of all other goods, including housing and an aggregate of food,
other goods, and services with an elasticity of substitution, σch, equal to 1.0. The food, goods, and services aggregates
have an elasticity of substitution, σc, of 0.5. Housing includes an energy bundle (coal, gas, electricity, and oil) with
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an elasticity of substitution, σhe, of 0.5. Within housing, energy and capital have an elasticity of substitution, σhk, of
1.0. The demand structure and elasticity of substitution above are discussed in the ADAGE documentation (Cai et al.,
2023) and shown in Fig. 4.
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Fig. 4. Model structure for household demand.
This figure depicts the structure for household demand by displaying its inputs and connecting them with lines to
indicate how they are combined in the household utility function. Each vertex includes inputs whose substitution is
parameterized by the elasticity displayed for that vertex.
Transportation aggregates, composed from passenger transportation and nonpassenger transportation, substitute
with an aggregate of all other goods and services at an elasticity of 0.5 (σct). Households cannot substitute between
freight and passenger transportation (σtr = 0) nor among modes of freight (e.g., water, train, truck, and other; σtf = 0).
Household substitution among passenger modes (e.g., airline, bus, train, and auto) is possible with a substitution of
σtp = 0.5. Within transportation modes, transportation vintages and technologies produce undifferentiated goods
that compete freely with one another (implied elasticity σtpf = ∞) constrained only by their fixed factor and capital
stocks.
Transportation service demand in each region is a function of GDP per capita, population, and region-specific
preferences among travel modes. Based on (Dargay et al., 2007), we used the S-shaped Gompertz function to project
the relationship between vehicle ownership growth rate and per capita income, urbanization, and population for all
ADAGE regions over the model horizon. The calculated trend in vehicle ownership was then used to calibrate
ADAGE's trend in household demand for light-duty and passenger bus transportation service as a fraction of income
over time. Household passenger service demand is mainly driven by population growth in the United States with
modest per capita VMT growth, whereas in China and Africa, both GDP and population growth drive VMT growth
rates. The passenger vehicle ownership growth trend is shown in Table A2 in Appendix A, with more details
presented in the ADAGE documentation (Cai et al., 2023).
3.1.3. Electricity production and the energy sector
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The rapid cost reduction of AFVs in recent years and assumed in the future (Fig. 3) indicates that AFVs may take an
increasingly significant role in the transportation sector in the future. The substitution of other energy sources,
especially electricity for liquid fuels, means that representation of future energy production plays a vital role in
analyzing changing technologies in the transportation sector and in determining implications for net GHG
emissions. Ten electricity generation technologies (conventional coal, natural gas, refined oil, combined cycle
natural gas and renewable electricity from solar, wind, nuclear, hydro, geothermal, and biomass) are included in
ADAGE. Each technology has an independent CES production function. ADAGE includes fixed factors of production
in the top nest of the electricity production function for renewable and combined cycle natural gas generation. Price
competition across different electricity generation technologies is limited by an aggregation elasticity. Consumers
purchase one undifferentiated electricity good. The electricity generation cost for these technologies was taken
from the 2020 Annual Technology Baseline from U.S. National Renewable Energy Laboratory (2020).
In the energy sector of ADAGE, fixed factors are incorporated into the production functions for coal, natural gas, and
crude oil used to represent natural resource constraints. Crude oil is assumed to be a homogeneous product trading
at a single world price, with adjustments for tariffs, export taxes, and transport margins. Armington aggregation
differentiates between coal, gas, refined oil, and electricity from domestic production and imports from other
regions of the world.
3.2. Scenario implementation
In this paper, we present two versions of ADAGE: the NoAFV version, which includes eight modes of transportation
(auto, freight truck, bus, air, rail freight, rail passenger, water, and all other transportation) with only conventional
technologies available for all modes, and the YesAFV version, which includes the same eight modes of transportation
as NoAFV and incorporates AFV technologies in all modes of on-road transportation (auto, bus, freight trucks) in all
regions. Technology costs in the NoAFV version are representative of conventional technologies. In the base year of
the YesAFV version, only conventional technologies are calibrated to be operating, but new vehicles are chosen
endogenously based on cost, meaning AFVs could be produced immediately if economical.
We then present illustrative oil price scenarios under the hypothesis that different assumptions about long-term
future oil prices will significantly affect the modeled energy and transportation system results for each ADAGE
version. The crude oil price paths in these sensitivity scenarios were taken from the U.S. EIA 2018 AEO, specifically
the Reference Oil Price case (referred to hereafter as REF), High Oil Price case (hereafter HOP), and Low Oil Price case
(hereafter LOP) (see Fig. 5).3
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Fig. 5. Crude oil price cases from AEO2018 (2010$/Barrel).
This line graph shows the crude oil price in 2010$ for each model year (2010 to 2050). The low oil price (LOP) case
declines from 2010 to 2020, then experiences a slight incline each year after. The reference (REF) case and high oil
price (HOP) case show an increase in price for all years with the greatest increase occurring until 2025.
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Those oil price cases assume a range of exogenous global crude oil price paths for each model version. The REF
represents an increasing trend projection for both global oil supply and demand with crude oil prices rising steadily
across the projection period from $62/barrel in 2020 to $101/barrel in 2050. The HOP and LOP encompass a wide
and asymmetric range of crude oil prices to demonstrate ADAGE behavior under an array of plausible fuel price
paths. Prices diverge after 2015, bounding the REF price path and illustrating variations in modeled global demand
for and supply of, among other things, petroleum and other liquid fuels, energy as a whole, and transportation
service.
4. Results
In this section, we examine how adding alternative-fuel technologies alters the ADAGE REF case results for VMT,
vehicle stock fuel efficiency, energy markets, and GHG emissions. Although ADAGE is a global model, we focus on
results for the United States. We then examine technology trends across transportation modes under AEO 2018 REF,
LOP, and HOP to understand how changes in oil prices can alter U.S. economic and environmental outcomes such as
changes in VMT, AFV adoption, and GHG emissions. As ADAGE's 5-year time steps model economic behavior for a
representative year within the period, the results shown do not correspond to the exact year for which they are
reported (e.g., 2020, 2025). ADAGE results are most comparable to the average economic activity for the year
presented and the four years following it (e.g., the 2020 time step is an average of 2020–2024). Our model
simulations of these scenarios are intended to illustrate the sensitivity of transportation technology adoption to
different market outcomes. Our results are not intended to project or exactly replicate real economic outcomes. The
value in this sort of modeling is to understand insights from economic outcomes by order of magnitude, direction,
and relative change, but not in exact levels.
4.1. Reference oil price scenario
4.1.1. U.S. VMT
In both versions of our REF case, the NoAFV version and the YesAFV version, VMT grows for each vehicle type over
time (Fig. 6). In the NoAFV version, U.S. light-duty VMT demand comes mostly from households, and the growth rate
of 0.7% per year (from 2613 billion miles in 2020 to 3180 billion miles in 2050) reflects ADAGE's calibrated decline in
households' transportation demand per unit of consumption over time. Freight truck demand is proportional to U.S.
economic output, and its growth rate is the highest of the three on-road modes (1.8% per year, resulting in an
increase from 301.5 to 511.3 billion miles between 2020 and 2050). Passenger bus demand grows by 1.3% per year
(from 9.9 billion miles in 2020 to 14.4 billion miles in 2050).
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Fig. 6. Vehicle miles traveled in the United States, reference oil price projections (billion VMT).
This bar chart shows total vehicle miles traveled (VMT) for light duty, freight truck, and bus. Bars include stacked
components representing conventional and alternative fuel vehicles and are separated by YesAFV and NoAFV. Total
VMT is completely replaced by hybrid or battery vehicles s by 2050 in the YesAFV version, and conventional is
substantially reduced in 2040 for light duty vehicles (1339 billion VMT in 2030 to 242 billion VMT in 2040).
Including additional technologies in the model in the YesAFV version results in faster growth in LDV (0.9% per year)
and passenger bus demand (1.1% per year) and a decline in the growth of freight demand (1.7% versus 1.8% per year
in the NoAFV version). The YesAFV version also results in 100% replacement of conventional vehicles by hybrids and
BEVs by 2050 across auto, freight truck, and passenger bus modes (Fig. 6). As described in Fig. 3 above, based on our
assumptions about improvements in their efficiency and declining capital costs, total transportation service cost for
BEVs and hybrids becomes lower than conventional vehicles over the 2020–2030 period and continues to decline
through 2050. Higher projected capital costs for fuel cell vehicles and the assumed lower fuel efficiency of natural
gas vehicles lead to higher costs per mile for these two technologies. This results in minimal penetration across all
three modes in these results, although fuel cell vehicles do play some role in freight trucking.
Replacement of conventional vehicles is also driven by the retirement of older vehicles, either at the end of their
useful life or earlier via economic retirement. Economic retirement occurs in the ADAGE model if the cost to operate
an existing vehicle is higher than the cost to operate and cover capital payments on a new vehicle. Economic
retirements occur for some conventional and hybrid vehicles in the 2030–2050 time frame. Economic retirement of
conventional passenger bus and freight trucks in the YesAFV version is evident after 2040 from conventional vehicle
VMT declines that are larger than would occur from end-of-life retirements alone.
The roles of hybrids and BEVs in the transition to AFVs vary by mode. For LDVs, the YesAFV results show hybrid
vehicles serving as a “transition” technology from 2020 to 2040. These results show BEVs gaining greater adoption
after 2030 and becoming the dominant technology by 2050. Hybrid vehicles play a smaller role in freight truck and
passenger bus segments of the U.S. transportation sector. Instead, these modes transition more directly from
conventional technology to battery electric technology. Our assumptions about technology costs and the flexibility
of consumer preferences influence the timing of the transition to AFVs and highlight areas of uncertainty in this
transition. As we discuss below, additional research should further explore the issue of the transition to AFVs.
The availability of lower-cost LDVs in later modeled periods leads to increases in passenger VMT but decreases in
freight truck VMT in the YesAFV version relative to the NoAFV version. The ADAGE structure allows for all global
regions to have the same ability to purchase AFV technologies. The availability of efficient AFVs causes non-U.S.
regions to rely more on domestic freight transportation production instead of imports from the United States.
Increased freight truck VMT over time is met by a growing share of EVs rather than by hybrid vehicles.
4.1.2. U.S. fuel efficiency
Absent alternative-fuel technologies, the vehicle fleet within each mode can only improve efficiency by investing in
improvements for new conventional vehicles and then retiring older, less efficient vehicles. In addition to
exogenous efficiency improvements for new vehicles over time, ADAGE allows for endogenous efficiency
improvements for new conventional vehicles in response to the relative prices of capital and fuel, and the NoAFV
version uses this ability to increase vehicle efficiencies in the REF case to some extent. With AFVs, the model
outcomes for fleet fuel efficiency improvements parallel hybrid and BEV technology adoption across modes, leading
to larger fuel efficiency improvements by 2050 for LDVs, freight trucks, and buses (see Fig. 7). More rapid EV
adoption around 2040 produces visible inflection points in aggregate fuel efficiency increases in the YesAFV version.
Concurrently, the introduction of light-duty AFVs reduces pressure on the transportation sector to invest in
conventional vehicle efficiency after 2020. Under the assumptions used in the YesAFV version, the ADAGE model
simulates that the least-cost solution is to concentrate investment capital in the relatively more efficient BEVs and
hybrids. As a result, conventional vehicle fuel economy does not grow as rapidly after 2020 as it does in the NoAFV
version.
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Fig. 7. U.S. fleet-wide fuel economy for on-road transportation in the United States (Miles/MMBTU).
These line graphs show fuel economy in miles per million metric British thermal units (MMBTU) for years 2020
through 2050. The graphs are separated by light duty, freight truck, and bus. The lines are separated by conventional
and fleet average, with fleet average shown for the YesAFV and NoAFV versions. Both conventional and fleet average
fuel economy are highest for light duty and average YesAFV is highest for most years for all vehicle types. Average,
YesAFV has the greatest increasing slope over time for all vehicle types, especially light duty.
For freight trucks and passenger buses, assumed differences in conventional and AFV costs are such that
conventional vehicles remain the least-cost solution for most of the modeled period. Like LDVs, freight trucks and
buses are subject to U.S. federal vehicle fuel efficiency standards (discussed in Section 3.1.1). The assumed efficiency
standard targets for trucks and buses are much closer to the modeled future conventional technology efficiencies in
these results. Endogenous changes in conventional freight truck and bus efficiency are therefore smaller than those
for LDVs.
4.1.3. U.S. energy markets and GHG emissions
In the NoAFV version, energy consumption in the U.S. transportation sector grows at a gradual rate of 0.5% per year,
from 25.4 quads in 2020 to 29.9quads in 2050. Even without AFVs, LDV energy consumption declines as efficiency
improvements more than offset the slight rise in light-duty VMT (see Fig. 8-a). The introduction of AFVs in the
YesAFV version leads to a net increase in VMT across modes but a significant decrease in total energy consumption
(see Fig. 8-a and -d). This result is driven by the marked rise in energy efficiency for all on-road modes over the
2020–2050 period. The rest of the U.S. transportation sector (air, water, rail, and other modes), where AFVs were not
assumed to be an option in these scenarios, accounts for a growing fraction of total U.S. transportation energy
demand and most U.S. transportation energy demand by 2050. Most of the energy demand from other U.S.
transportation modes is served by fossil fuels. Including AFVs for these modes in the model could produce results
with reduced energy use for these subsectors, as occurs for on-road vehicles in the results presented here,
depending on the projected assumptions made about those AFVs.
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Fig. 8. Level and change in transportation energy consumption (a, d), Electricity generation by technology (b, e), and
GHG emissions by sector (c, f).
This figure shows six bar graphs representing energy consumption, electricity generation, and GHG emissions for
model years 2020 to 2050. Total energy consumption increases from one year to the next, with the greatest
consumption occurring in transportation vehicles that are not light duty, freight, or bus for all years in the YesAFV
version. For the NoAFV version, rest of transportation has the highest consumption in all years but 2020. Electricity
generation by technology shows a decline in coal from 2020 to 2040 with a slight increase in 2050 for both YesAFV
and NoAFV. Combined cycle gas takes up the greatest portion of the graph for all years expect for 2020, where
conventional coal is the greatest, for both YesAFV and NoAFV versions. GHG emissions is highest for sectors not
included in the model for all years and for both YesAFV and NoAFV versions. Emissions from fright truck is only
present in the NoAFV version and shows an increase in emissions every year from 2020 to 2050.
The expansion of EV technology also has a marked impact on U.S. electricity generation by 2050; in the YesAFV
version, electricity generation in 2050 is estimated to be 28.8% higher than in the NoAFV version (see Fig. 8-b and e). ADAGE's cost projections for the U.S. electricity sector show electric generation capacity expansion that
proportionally favors more efficient, lower GHG-emitting combined cycle natural gas plants and solar and wind
power over coal-fired electricity generation. However, whereas coal is a proportionally smaller share of the 2050
energy mix than the 2020 energy mix in the YesAFV version, electricity generation from coal is slightly higher in
these results than in the NoAFV version. In ADAGE, this can be interpreted as delayed retirement of preexisting coal
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generation capacity rather than construction of new capacity. As electricity demand rises in the YesAFV version, so
do electricity prices.
Total U.S. GHG emissions in the NoAFV version rise by 8.2 (6970 to 7589 million metric tons of carbon dioxide [MMT
CO2] equivalence [CO2-e]) from 2020 to 2050: most of the increase comes from outside the on-road transportation
and electricity sectors. The introduction of AFVs slows down the increase of total U.S. GHG emissions by 0.7
percentage points (from 7042.14 to 7062.95 MMT CO2-e) during 2020–2050, where the largest reduction comes
from light-duty, freight truck, and passenger bus transportation (1084to 117. MMT CO2-e by 2050; see Fig. 8-c and f). The net effect on total annual U.S. GHG emissions in 2050 is a 6.9% decrease from the NoAFV to the YesAFV
version. U.S. electricity GHG emissions in 2050 are higher in the YesAFV version than in the NoAFV version (2107
MMT CO2-e in the YesAFV version versus 1672 MMT in the NoAFV version) owing to increases in transportation
sector demand and moderated by relative declines in coal that reduce electricity sector emissions intensity. The
higher energy efficiency of AFV technologies and declining electricity generation GHG emissions intensity over the
time period modeled mitigate additional emissions associated with the increase in transportation electricity
demand in the YesAFV results (see Fig. 8-c). In both versions, other transportation (especially air transportation) and
other sectors (especially manufacturing and household demand for energy) contribute to rising emissions.
4.2. Alternative oil price cases
In this section, we examine the impacts of different oil price paths on U.S. transportation and energy sector
outcomes and net GHG emissions in the two ADAGE versions. As shown in Fig. 5, the HOP path from EIA has crude
oil prices that are approximately double the prices in the REF case by 2050, and the LOP case has crude oil prices
that are roughly half the REF case by 2050.
The HOP case results in a 15% reduction in light-duty VMT in 2050 relative to the REF case in both the NoAFV and
YesAFV versions of the model (Fig. 9). The comparable percentage reduction in VMT across different assumptions
about the availability of AFVs suggests that lower light-duty VMT stem more from income effects on U.S.
households under high oil prices than from substitution effects associated with higher VMT costs. Conventional
LDVs and buses also phase out sooner in the HOP case than in the REF case. Hybrid AFVs play less of a transition
technology role for LDVs with higher oil prices, as BEVs gain a comparative cost advantage over hybrids sooner with
higher oil prices. Low oil prices induce proportionally opposite behavior; LDV hybrids serve longer as a transition
technology (see Fig. A2 in Appendix A).
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Fig. 9. Change in U.S. VMT in HOP case relative to REF case (Billion VMT).
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This bar chart shows change in the high oil price (HOP) case relative to the reference (REF) case for vehicle miles
traveled (VMT) for each model year (2010 to 2050). Net change overall is greater for light duty and freight truck
than bus, with negative change for light duty and positive for freight truck.
Freight truck VMT increases by around 14% with higher oil prices. The historical data on which ADAGE is built reveal
that the United States has a relatively more energy-efficient manufacturing sector than most global competitors,
such as China and Brazil (World Bank, 2012). Rising oil prices therefore tend to benefit the United States, improving
competitiveness for the United States relative to foreign manufacturers and increasing U.S. manufacturing output
(Fig. 10) and manufacturing demand for freight (as well as increasing GDP; see Fig. A4 in Appendix A). In the HOP
case, U.S. net exports increase by $2–4 trillion between 2020 and 2050 compared to net exports over the same
period in the REF case, leading to a higher freight VMT. Freight VMT is proportionally lower in the LOP case with
lower U.S. manufacturing demand. Consistent with light-duty and bus technology composition, in the YesAFV
version, the increased VMT for freight trucks is met by a larger share of EVs than by hybrid technology.
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Fig. 10. Change in U.S. Economic output by sector in HOP case relative to REF case ($Billion).
This bar graph shows a change in high oil price (HOP) relative to the reference (REF) case with stacked bars
representing conventional and electric vehicles from 2020 to 2050. Difference in output from REF for manufacturing
is the greatest among manufacturing, followed by light duty.
In the early periods of the HOP case in the YesAFV version, most of the VMT reduction comes from conventional
vehicles, which are not offset by AFVs. Minimal AFV adoption in early periods means fleet fuel efficiency
improvements are limited to the endogenous efficiency response of new conventional vehicles, which are also
being added at a slower rate as a result of lower VMT growth. In later periods in the HOP case, increased adoption of
EVs improves fleet efficiency further relative to the REF case, but because this is primarily at the expense of hybrid
vehicles, the fleet-wide efficiency improvements are modest. Conversely, relatively small decreases in U.S. fuel
efficiency occur in the LOP case, with less endogenous efficiency improvements and slower AFV adoption,
particularly for LDVs.
Freight truck vehicles, whose VMT increase in the HOP case in the NoAFV and YesAFV versions and where EV
adoption does not begin until the 2040s, show little efficiency gains with higher oil prices, until a slight increase in
the early 2040s. Freight truck EV adoption is rapid in 2040–2050, leaving relatively little room for large
compositional changes that would significantly change efficiency in either the LOP or HOP case.
In both the NoAFV and YesAFV versions, U.S. transportation energy consumption declines by approximately 5 quads
by 2050 in the HOP relative to the REF case, and about one-fifth of that decline is due to LDVs and the rest from the
rest-of-transportation sector (Fig. 11). AFVs allow for slightly larger reductions in LDV energy consumption in 2020–
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2040 in the HOP case from earlier conventional vehicle retirement and EV adoption in the REF case. Reductions in
U.S. transportation energy demand come from refined oil over the entire period, but this reduction is slightly offset
by small increases in biofuels, which can be blended with oil for use in conventional and other vehicles.
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Fig. 11. Change in U.S. Transportation energy consumption, HOP case relative to REF case (Quad BTU) This stacked
bar graph shows differences in Quad BTU from the reference case (REF) comapred to the high oil price (HOP) case
by transportation type. Most model years are included (2020 to 2050) and there is a seperation by YesAFV and
NoAFV. Light duty and the rest of transportation sectors are negative, where freight truck is positive. Bus is negative
for all years and versions expect for NoAFV in 2050.
Net U.S. GHG emissions decrease under higher oil prices both with and without AFVs (Fig. 12), although the
reduction relative to REF from LDVs is smaller in later years than earlier years with AFVs because most of the onroad vehicle inventory has already been electrified by 2050 (Fig. 6). Total U.S. GHG mitigation of approximately 960
MMT CO2-e in 2050 represents a decline of approximately 13% of total GHG emissions relative to REF for the United
States from roughly doubling oil prices. Conversely, low oil prices lead to a 296–334 MMT CO2-e increase in
emissions by mid-century relative to REF (Fig. A3 in Appendix A). ADAGE assumptions and structure project that
roughly doubling oil prices in HOP relative to REF by 2050 leads to a percentage reduction in U.S. transportation
sector GHG emissions of 34% in YesAFV and 18% in NoAFV. Roughly halving oil prices in LOP relative to REF leads to
an increase in U.S. transportation sector GHG emissions of 35% in YesAFV case and 14% in NoAFV case.
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Fig. 12. Change in U.S. GHG emissions by sector, HOP case relative to the REF case (MMT CO2-e).
This bar graph shows differences in GHG emissions from the reference (REF) case comapred to the high oil price
(HOP) by transportation sector. The overall net change is positive for 2020, then remains mostly netural for 2030
and 2040 before showing a negative change in 2050.
4.3. Sensitivity analyses
We examined the sensitivity of our results to changes in the assumptions for two key components of our model, the
fixed-factor substitution elasticity for alternative fuel vehicles and the structure of household consumption. We
found that our core results were sensitive to the value of the fixed factor elasticity and largely insensitive to an
alternative consumption structure.
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For the transportation fixed factor elasticity, we conducted a set of two sensitivity analyses setting the fixed factor
elasticities at 75% and 200% the current value. Model results are indeed sensitive to this parameter in the expected
directions, i.e., higher elasticities lead to greater alternative fuel vehicle penetration and scrappage of conventional
vehicles. There was one notable exception where elasticity results did not move in the expected direction: lightduty BEV adoption in the low oil price scenario declined in the scenario with the highest assumed elasticity.
Instead, HEVs gained greater market share earlier in this scenario and remained cost competitive with BEVs under
low assumed oil price conditions. BEVs still gained market share in later model periods but achieved a lower final
market share due to crowding out from HEVs, relative to what we observe with lower elasticity assumptions. This
result is due in part to the dynamics of the fixed factor and was not present in other oil price scenarios or
transportation modes. Truck and bus AFV adoption appears more sensitive to the elasticity assumption. The relative
costs of ICEs and BEVs are closer for trucks and buses than for other modes, making consumer propensity to adopt a
more important parameter. In the 75% elasticity scenario, BEV trucks and buses achieve only slight adoption even in
2050, relative to the base case of complete AFV penetration by that year. Similarly, the 200% elasticity leads to a
cessation of conventional vehicle production in the 2030s instead of 2040s. Appendix B contains figures
summarizing the results of the fixed factor elasticity sensitivity analyses.
We also explored an alternative specification for consumer demand for transportation. While demand systems have
been an active area of research in the econometrics literature, their applications in economy-wide modeling are less
prevalent. More work needs to be done to estimate alternative consumption structures with respect to
transportation. Structures like the one we have adopted are used in other economy-wide models (e.g., EPPA).
However, as we have emphasized in the paper, technologically detailed transportation sectors are not common to
these models, perhaps weakening the motivation of past research to invest more effort in the specification of
transportation consumption structures.
As a sensitivity, we nested passenger transportation with housing under the notion that people substitute away
from expensive housing toward more commuting (e.g., move farther out of the urban core) and freight with
consumption goods purchases. Although changes in results were very modest, we believe that alternative
consumption structures could be an area worthy of further research and investigation. The supplemental
information includes the alternate consumption structure and sensitivity results for VMT by transportation mode
and technology.
5. Discussion
Inclusion of AFV technologies in the transportation sector in economic models is of increasing importance given
their growing presence in global markets and the potential impacts they may have on energy consumption and
GHG emissions. Our results suggest that including AFV technologies in a CGE model may alter the modeled energy
and emissions outcomes; for example, adoption of BEVs may increase economy-wide electricity usage. Given the
cost and performance characteristics of AFVs assumed in this analysis, including AFVs in ADAGE's on-road
passenger and freight sectors produces significant changes in the projected level of U.S. transportation service
demand, sector fuel efficiency, energy consumption, and GHG emissions.
Cost and performance estimates for both battery packs and fuel cell stacks are changing frequently, and analyses
seeking to characterize the current or likely future state of these markets should endeavor to use the most up-todate estimates available. The ADAGE model methodology presented in this paper provides a framework within
which up-to-date vehicle cost and performance estimates can be used and continually updated for current analyses.
However, even the most up-to-date projections of cost and performance carry uncertainty (e.g., regarding the pace
of technological learning, the potential for technological breakthroughs). Modeled outcomes are likely to be
sensitive to these uncertain assumptions, which suggests that uncertainty analysis and model sensitivity analysis
are essential components of future work in this area. Future work could seek to characterize model parameter and
scenario sensitivity using the ADAGE model or other CGE models.
The impacts described in our results also vary depending on assumed future oil market conditions. Under our
assumptions, the model results suggest that BEVs could capture a substantial and growing market share in the
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coming decades and that hybrid technologies have the potential to play an important role in the next 10–15 years,
particularly in model scenarios with lower oil prices. ADAGE estimates show higher oil prices speeding up the
transition to EVs and shortening the intermediate transition role hybrid vehicles play. However, vehicles have
complex and capital-intensive supply chains with high fixed costs, and these supply chain capital dynamics are not
fully represented in ADAGE; thus, the results presented here should be viewed with caution from that perspective.
While the future cost and performance characteristics of AFV technologies remain uncertain, it is clear from our
results that exploration of potential future scenarios concerning energy consumption, GHG emissions, and
economic output in CGE modeling frameworks would benefit from greater transportation sectoral detail and
technological specificity. AFV technologies introduce new factors and dynamics to the transportation sector, such as
new linkages with the electricity sector, which more aggregated representations of the sector cannot capture well.
Leaving out AFV technologies limits the possibilities for changes in transportation sector and economy-wide energy
consumption, emissions, and economic output; frameworks that do so may miss potential shifts in all of these
outcomes in their scenario analyses. Our results demonstrate that these dynamics can and should be considered in
CGE modeling frameworks, regardless of the specific cost, performance, or energy market or macroeconomic
assumptions used to conduct a specific scenario analysis.
6. Conclusions
The U.S. transportation sector is anticipated to undergo vast structural changes over the next several decades
because of emerging market forces and evolving responses to the issue of climate change. CGE models, which
examine the impacts of market forces on the whole economy, can be particularly useful in assessing emerging
economic trends. To date, CGE models addressing GHG emissions have carried the most technological and subsector
detail in the electricity sector portion of their models. Only a few CGE models have developed detailed
representations of the transportation sector that can address structural changes likely to occur in this segment of
the economy over the next several decades.
Calibrating the ADAGE model to AEO 2018 oil price paths, we examined a case based on one assessment of
projected future efficiencies and costs of AFVs. This analysis allowed for a demonstration of the novel
transportation sector methods employed in this enhanced version of ADAGE. Different assumptions about vehicle
efficiencies and costs lead to different model results for U.S. transportation sector outcomes and economy-wide
outcomes. As a result, the major contribution of this paper is to demonstrate, and provide insight into, how
transportation subsector and technological detail influences modeled economic and environmental outcomes in
this framework. The results presented in this paper indicate modeled outcomes based on cost assumptions and
model structure, not predictions of specific future outcomes. They provide a useful diagnostic tool for gaining
insight on likely directions and relative magnitudes of market and environmental outcomes under different
technology and cost assumptions.
The ADAGE model represents the whole economy, and the transportation and electricity sector are integrated with
one another. This feature makes the model well suited to estimate sectoral and economy-wide GHG impacts, as well
as GDP impacts, which might result from a modeled shift in on-road vehicle fleet composition given a set of
assumptions about technological options. Based on the assumptions used in this study, increased penetration of EVs
results in significant reductions in U.S. transportation sector GHG emissions, increases in U.S. electricity sector GHG
emissions, and reductions in economy-wide U.S. GHG emissions relative to results where ADAGE did not include
AFVs as an option. As expected, simulations with higher oil prices lead to more rapid penetration of AFVs, and lower
oil prices lead to slower penetration of AFVs.
This work shows the significance of including transportation technology representation in an economy-wide model
and provides an example of how this detail was applied to a particular CGE, ADAGE. However, the limitations to the
approach described here could be improved in ADAGE or other CGEs. Extending subsector and technological detail
introduced for on-road vehicles, as undertaken in this effort, throughout the whole transportation sector is a
valuable next step in identifying future market and environmental outcomes for the transportation sector overall.
Transportation modes in which technological detail could be added include air, rail, and marine. Each of these
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transportation modes will have a different set of technologies with different performance and cost characteristics
that would need to be considered in developing detailed, technological assessments. In addition, developing explicit
representation of hydrogen in a CGE model could allow for more detailed understanding of potential impacts of fuel
cell vehicles. For both AFVs already incorporated in ADAGE and for these potential new technology options, there
may be important network externalities that affect estimated technology penetration. In the current study, we
utilize fixed factors to account for such effects, but a more detailed characterization of technology adoption may be
a fruitful area for future research. Finally, analyzing criteria air pollutants in a CGE model would allow key
assessments of multipollutant effects of air quality and climate change simultaneously from alternative scenarios.
The transportation sector technological detail presented in this paper, as well as these areas for potential future
research, could help make CGE models more useful in understanding impacts of future shifts in the transportation
sector.
Role of the funding source
The work conducted for this article was done under support received through U.S. Environmental Protection Agency
(EPA) contracts EP-C-16-021 and 68HERH19D0030. Three of our authors are employed by EPA and administered the
project supporting its completion. EPA authors' contributions are documented in the CRediT author statement.
CRediT authorship contribution statement
Yongxia Cai: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration,
Validation, Visualization, Writing – original draft, Writing – review & editing. Jared Woollacott: Formal analysis,
Investigation, Methodology, Project administration, Supervision, Writing – review & editing. Robert H. Beach:
Conceptualization, Formal analysis, Investigation, Methodology, Writing – original draft, Writing – review & editing.
Lauren E. Rafelski: Conceptualization, Funding acquisition, Methodology, Project administration, Supervision,
Validation, Writing – review & editing. Christopher Ramig: Conceptualization, Funding acquisition, Methodology,
Validation, Writing – review & editing. Michael Shelby: Conceptualization, Methodology, Validation, Writing –
review & editing.
Declaration of Competing Interest
None.
Acknowledgments
The views expressed in this article are those of the authors and do not necessarily represent the views or policies of
the U.S. Environmental Protection Agency. The authors gratefully acknowledge Page Kyle from Pacific Northwest
National Laboratory for providing us with transportation related data, Avery Tilley from RTI for data visualization
and literature research, and Jim McFarland and Gloria Helfand from the U.S. Environmental Protection Agency for
providing helpful comments on the paper.
Appendix A
Table A1. Fuel economy for all on-road new vehicles in the United States from 2010 to 2050 (Miles/million British
thermal units [MMBTU]).
2010
2015
2020
2025
2030
2035
2040
2045
2050
Conventional
204
210
231
291
292
292
290
288
284
Natural gas
194
198
222
284
287
287
287
286
282
Battery
630
646
714
775
777
782
785
785
782
Light-duty (miles/MMBTU)
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2010
2015
2020
2025
2030
2035
2040
2045
2050
Hybrid
338
348
383
463
463
461
458
453
446
Fuel cell
461
467
474
479
485
492
499
507
515
Freight truck (miles/MMBTU)
Conventional
69
74
77
80
83
85
86
87
88
Natural gas
62
67
69
72
75
77
77
78
80
Battery
207
222
231
240
249
255
259
262
265
Hybrid
86
92
96
100
104
106
108
109
110
Fuel cell
138
148
154
160
166
170
172
175
177
Conventional
54
55
62
66
68
72
75
79
83
Natural gas
49
49
55
59
61
64
68
71
75
Battery
214
220
246
264
273
287
301
316
333
Hybrid
81
82
92
99
102
107
113
119
124
Fuel Cell
107
110
123
132
136
143
151
158
166
Bus (Miles/MMBTU)
Table A2. Passenger vehicle ownership growth trend by ADAGE regions (1 in 2010).
2010
2015
2020
2025
2030
2035
2040
2045
2050
United States
1
1.01
1.06
1.10
1.14
1.18
1.21
1.24
1.27
Brazil
1
1.20
1.42
1.63
1.83
2.00
2.16
2.30
2.42
China
1
1.40
1.95
2.47
2.96
3.41
3.81
4.16
4.44
European Union
1
1.05
1.09
1.13
1.16
1.19
1.21
1.23
1.24
Rest of Latin America
1
1.31
1.69
2.09
2.47
2.85
3.22
3.57
3.89
Rest of Asia
1
1.26
1.56
1.86
2.15
2.44
2.71
2.97
3.20
Africa
1
1.70
2.85
4.15
5.61
7.24
9.05
11.01
13.11
Rest of World
1
1.24
1.48
1.71
1.9
2.14
2.35
2.55
2.73
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Fig. A1. Comparison of Key Results in Two Versions of the Model (NoAFV: Version with eight modes of
transportation and no alternative-fuel technologies; Agg: Version with two modes of transportation with light-duty
and aggregated transportation from the other seven modes and no alternative-fuel technologies) in the REF
Scenario: a. U.S. Transportation Energy Consumption by Mode in (Quad BTU); b. U.S. Electricity Generation by
Generation Technology (Quad BTU); c. U.S. GHG Emissions by Sector (MMT CO2-e).
This figure has three bar graphs showing two versions of the model (NoAFV and Agg) for the reference (REF) case
for years 2020 to 2050. Transportation vehicles that are not light duty, freight, or bus have the most energy
consumption in all years and case expect for NoAFV 2020 where light duty consumes the most. Fright truck and bus
only appear in the NoAFV case. Electricity generation is highest for combined cycle gas and conventional coal, until
2050 where the highest is combine cycle gas and solar. Most GHG emissions are the result of the other sectors
category for all years modeled.
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Fig. A2. Change in U.S. vehicle miles traveled in LOP case relative to REF case (billion VMT).
This figure shows the difference in vehicle miles traveled (VMT) from the reference (REF) case compared to the low
oil price (LOP) case from 2020 to 2050. Light duty shows a positive net change, freight truck shows a negative net
change, and bus has a relatively neutral net change.
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Fig. A3. Change in U.S. GHG emissions by sector, LOP case relative to the REF case (MMT CO2-e).
This bar graph shows the low oil price (LOP) case relative to the reference (REF) case in change in GHG emissions by
sector. The overall net change is negative for 2020 then remains positive for 2030 through 2050. Light duty, other
transportation, bus, electricity production, and freight truck are always positive.
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Fig. A4. GDP growth trend by region and scenario (0 in 2010) (a) and change in GDP in YesAFV from NoAFV (b)
($Billion).
This figure has four line graphs showing GDP growth tends by oil price case and change in GDP from NoFAV version.
GDP trends up in all cases, both in the U.S. and in other countries, for all years. The high oil price (HOP) case shows
the greatest growth in GDP in the U.S. but the least in other countries, where the low oil price (LOP) case shows the
greatest growth. Change in GDP from the NoAFV version increase in both the U.S. and Non-U.S. countries for all
years, with a greater initial increase in Non-U.S. countries.
Appendix B
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Fig. B1. Light-duty passenger transportation by technology, fixed factor elasticity, and scenario (billion VMT).
This figure provides a three-by-three stacked bar graphs. The height of each column is the total vehicle miles
traveled in a time period. The components of each bar represent the different vehicle technologies. Each row is a
different scenario, low oil price, reference, and high oil price from top to bottom. The first column is our 75 %
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elasticity, the second is our reference results, and the third is our two-times elasticity sensitivity results. Total VMT
is largely unchanged across sensitivity and reference results, but the technology mix varies with generally more
non-ICE vehicle penetration for higher elasticity values.
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Fig. B2. Road freight transportation by technology, fixed factor elasticity, and scenario (billion VMT).
This figure provides a three-by-three stacked bar graphs. The height of each column is the total vehicle miles
traveled in a time period. The components of each bar represent the different vehicle technologies. Each row is a
different scenario, low oil price, reference, and high oil price from top to bottom. The first column is our 75 %
elasticity, the second is our reference results, and the third is our two-times elasticity sensitivity results. Total VMT
is largely unchanged across sensitivity and reference results, but the technology mix varies with generally more
non-ICE vehicle penetration for higher elasticity values.
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Fig. B3. Bus passenger transportation by technology, fixed factor elasticity, and scenario (billion VMT).
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This figure provides a three-by-three stacked bar graphs. The height of each column is the total vehicle miles
traveled in a time period. The components of each bar represent the different vehicle technologies. Each row is a
different scenario, low oil price, reference, and high oil price from top to bottom. The first column is our 75 %
elasticity, the second is our reference results, and the third is our two-times elasticity sensitivity results. Total VMT
is largely unchanged across sensitivity and reference results, but the technology mix varies with generally more
non-ICE vehicle penetration for higher elasticity values.
Appendix C
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Fig. C1. Alternative consumption structure.
This figure depicts the alternative structure for household demand used for our sensitivity analyses by displaying its
inputs and connecting them with lines to indicate how they are combined in the household utility function. Each
vertex includes inputs whose substitution is parameterized by the elasticity displayed for that vertex.
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Download : Download high-res image (266KB)
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Fig. C2. Light-duty vehicle transportation by technology, consumption structure, and scenario (billion VMT).
This figure provides a three-by-two stacked bar graphs. The height of each column is the total vehicle miles traveled
in a time period. The components of each bar represent the different vehicle technologies. Each row is a different
scenario, low oil price, reference, and high oil price from top to bottom. The first column is our reference results and
the second is our results with the alternative transportation structure. Total VMT and technology mix is largely
unchanged across sensitivity and reference results.
Download : Download high-res image (237KB)
Download : Download full-size image
Fig. C3. Road freight vehicle transportation by technology, consumption structure, and scenario (billion VMT).
This figure provides a three-by-two stacked bar graphs. The height of each column is the total vehicle miles traveled
in a time period. The components of each bar represent the different vehicle technologies. Each row is a different
scenario, low oil price, reference, and high oil price from top to bottom. The first column is our reference results and
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Insights from adding transportation sector detail into an economy-wide model: The case of the ADAGE CGE model - ScienceDirect
the second is our results with the alternative transportation structure. Total VMT and technology mix is largely
unchanged across sensitivity and reference results.
Download : Download high-res image (246KB)
Download : Download full-size image
Fig. C4. Bus vehicle transportation by technology, consumption structure, and scenario (billion VMT).
This figure provides a three-by-two stacked bar graphs. The height of each column is the total vehicle miles traveled
in a time period. The components of each bar represent the different vehicle technologies. Each row is a different
scenario, low oil price, reference, and high oil price from top to bottom. The first column is our reference results and
the second is our results with the alternative transportation structure. Total VMT and technology mix is largely
unchanged across sensitivity and reference results.
Recommended articles
Data availability
Data used in this paper were from ADAGE simulation results, which are available on the following
Zenodo site: (adding address when accepted). The site includes a public repository of ADAGE model and
documentation.
References
Akimoto et al., 2015 K. Akimoto, B.S. Tehrani, F. Sano, J. Oda, M.K.T. Masui, K. Oshiro
MILES (Modelling and Informing Low Emissions Strategies) Project: Japan Policy Paper
Research Insitute of Innovative Technology for the Earth, National Institute for Environmental Studies (2015)
Google Scholar ↗
Alexeeva-Talebi et al., 2012 V. Alexeeva-Talebi, C. Böhringer, A. Löschel, S. Voigt
The value-added of sectoral disaggregation: implications on competitive consequences of climate
change policies
Energy Econ., 34 (2012), pp. S127-S142, 10.1016/j.eneco.2012.10.001 ↗
View PDF
View article
View in Scopus ↗
Google Scholar ↗
Anenberg et al., 2019 S.C. Anenberg, J. Miller, D.K. Henze, R. Minjares, P. Achakulwisut
https://www.sciencedirect.com/science/article/pii/S0140988323002086#f0005
34/46
2023/5/12 11:11
Insights from adding transportation sector detail into an economy-wide model: The case of the ADAGE CGE model - ScienceDirect
The global burden of transportation tailpipe emissions on air pollution-related mortality in 2010
and 2015
Environ. Res. Lett., 14 (9) (2019), 10.1088/1748-9326/ab35fc ↗
Google Scholar ↗
Antimiani et al., 2013 A. Antimiani, V. Costantini, C. Martini, M.C. Tommasino
Gdyn-E: a dynamic CGE model for the economic assessment of long run climate policy
alternatives
IATRC 2013 Symposium, Seville, Spain, 2013 (2013)
https://econpapers.repec.org/paper/agsiatr13/152272.htm ↗
Google Scholar ↗
Armington, 1969 P.S. Armington
A theory of demand for products distinguished by place of production
Staff Papers Intern. Monetary Fund, 16 (1) (1969), 10.2307/3866403 ↗
Google Scholar ↗
Arrow and Debreu, 1954 K.J. Arrow, G. Debreu
Existence of an equilibrium for a competitive economy
Econometrica, 22 (3) (1954), pp. 265-290, 10.2307/1907353 ↗
Google Scholar ↗
Babatunde et al., 2017 K.A. Babatunde, R.A. Begum, F.F. Said
Application of computable general equilibrium (CGE) to climate change mitigation policy: a
systematic review
Renew. Sust. Energ. Rev., 78 (2017), pp. 61-71, 10.1016/j.rser.2017.04.064 ↗
View PDF
View article
View in Scopus ↗
Google Scholar ↗
BNEF, 2015 BNEF
Electric Vehicle Outlook 2015
Bloomberg NEF (2015)
Google Scholar ↗
Bosetti and Longden, 2013 V. Bosetti, T. Longden
Light duty vehicle transportation and global climate policy: the importance of electric drive
vehicles
Energy Policy, 58 (2013), pp. 209-219, 10.1016/j.enpol.2013.03.008 ↗
View PDF
View article
View in Scopus ↗
Google Scholar ↗
Britz and van der Mensbrugghe, 2016 W. Britz, D. van der Mensbrugghe
Reducing unwanted consequences of aggregation in large-scale economic models - a systematic
empirical evaluation with the GTAP model
Econ. Model., 59 (2016), pp. 463-472, 10.1016/j.econmod.2016.07.021 ↗
View PDF
View article
View in Scopus ↗
Google Scholar ↗
Brockmeier and Bektasoglu, 2014 M. Brockmeier, B. Bektasoglu
Model structure or data aggregation level: which leads to greater bias of results?
Econ. Model., 38 (2014), pp. 238-245, 10.1016/j.econmod.2014.01.003 ↗
View PDF
View article
View in Scopus ↗
Google Scholar ↗
Bulavskaya et al., 2016 T. Bulavskaya, J. Hu, S.M. Moghayer, F.G.D. Reynès
EXIOMOD 2.0: EXtended Input-Output MODel: A Full Description and Applications
https://www.sciencedirect.com/science/article/pii/S0140988323002086#f0005
35/46
2023/5/12 11:11
Insights from adding transportation sector detail into an economy-wide model: The case of the ADAGE CGE model - ScienceDirect
TNO, Den Haag (2016)
Google Scholar ↗
Cai and Arora, 2015 Y. Cai, V. Arora
Disaggregating electricity generation technologies in CGE models: a revised technology bundle
approach with an application to the U.S
Clean Power Plan. ApEn, 154 (2015), pp. 543-555, 10.1016/j.apenergy.2015.05.041 ↗
View PDF
View article
View in Scopus ↗
Google Scholar ↗
Cai et al., 2023 Y. Cai, R. Beach, J. Woollacott, K. Daenzer
Documentation of the Recursive-Dynamic Version of the Applied Dynamic Analysis of the Global
Economy (ADAGE) Model
Technical Report (2023)
Google Scholar ↗
Calvin et al., 2016 K.V. Calvin, R. Beach, A. Gurgel, M. Labriet, A.M. Loboguerrero Rodriguez
Agriculture, forestry, and other land-use emissions in Latin America
Energy Econ., 56 (2016), pp. 615-624, 10.1016/j.eneco.2015.03.020 ↗
View PDF
View article
View in Scopus ↗
Google Scholar ↗
Capros et al., 2013 P. Capros, D. Van Regemorter, L. Paroussos, P. Karkatsoulis, C. Fragkiadakis, S. Tsani, I. Charalampidis, T.
Revesz, M. Perry, J. Abrell
GEM-E3 Model Documentation
https://publications.jrc.ec.europa.eu/repository/handle/JRC83177 ↗ (2013)
Google Scholar ↗
Caron, 2012 J. Caron
Estimating carbon leakage and the efficiency of border adjustments in general equilibrium—does
sectoral aggregation matter?
Energy Econ., 34 (2012), pp. S111-S126, 10.1016/j.eneco.2012.08.015 ↗
View PDF
View article
View in Scopus ↗
Google Scholar ↗
Château et al., 2014 J. Château, R. Dellink, E. Lanzi
An Overview of the OECD ENV-Linkages Model: Version 3
Organisation for Economic Co-operation and Development (2014)
Google Scholar ↗
Chen et al., 2022 Y.-H.H. Chen, S. Paltsev, A. Gurgel, J.M. Reilly, J. Morris
The MIT EPPA7: A Multisectoral Dynamic Model for Energy, Economic, and Climate Scenario
Analysis
MIT Joint Program on the Science and Policy of Global Change (2022)
Google Scholar ↗
Clarke et al., 2016 L. Clarke, J. McFarland, C. Octaviano, B. van Ruijven, R. Beach, K. Daenzer, Martínez S. Herreras, A.F.P. Lucena,
A. Kitous, M. Labriet, A.M. Loboguerrero Rodriguez, A. Mundra, B. van der Zwaan
Long-term abatement potential and current policy trajectories in Latin American countries
Energy Econ., 56 (2016), pp. 513-525, 10.1016/j.eneco.2016.01.011 ↗
View PDF
View article
View in Scopus ↗
Google Scholar ↗
Clauss and Schubert, 2009 M. Clauss, S. Schubert
The ZEW Combined Microsimulation-CGE Model: Innovative Tool for Applied Policy Analysis
ZEW-Centre for European Economic Research (2009)
https://www.sciencedirect.com/science/article/pii/S0140988323002086#f0005
36/46
2023/5/12 11:11
Insights from adding transportation sector detail into an economy-wide model: The case of the ADAGE CGE model - ScienceDirect
Google Scholar ↗
Creutzig, 2015 F. Creutzig
Evolving narratives of low-carbon futures in transportation
Transp. Rev., 36 (3) (2015), pp. 341-360, 10.1080/01441647.2015.1079277 ↗
Google Scholar ↗
Cunha et al., 2020 B.S. Cunha, R. Garaffa, Â.C. Gurgel
TEA Model documentation
Escola de Economia de São Paulo, Fundação Getulio Vargas (2020)
Google Scholar ↗
Dargay et al., 2007 J. Dargay, D. Gately, M. Sommer
Vehicle ownership and income growth, worldwide: 1960-2030
Energy J., 28 (4) (2007), pp. 143-170
View in Scopus ↗
Google Scholar ↗
Després et al., 2018 J. Després, K. Keramidas, A. Schmitz, A. Kitous, B. Schade, A.R. Diaz-Vazquez, S. Mima, H.P. Russ, T.
Wiesenthal
POLES-JRC model documentation-updated for 2018 (no. JRC113757)
Joint Research Centre (2018)
Google Scholar ↗
Edelenbosch et al., 2017 O.Y. Edelenbosch, D.L. McCollum, D.P. van Vuuren, C. Bertram, S. Carrara, H. Daly, S. Fujimori, A.
Kitous, P. Kyle, Ó.E. Broin, P. Karkatsoulis, F. Sano
Decomposing passenger transport futures: comparing results of global integrated assessment
models
Transp. Res. D Transp. Environ., 55 (2017), pp. 281-293, 10.1016/j.trd.2016.07.003 ↗
View PDF
View article
View in Scopus ↗
Google Scholar ↗
Energy Information Administration (EIA), 2019 Energy Information Administration (EIA)
Need Full Citation
(2019)
Google Scholar ↗
Environmental Protection Agency et al., 2016 Environmental Protection Agency, National Highway Traffic Safety Administration,
Department of Transportation
Final Rule for Phase 2 Greenhouse Gas Emissions and Fuel Efficiency Standards for Medium- and
Heavy-Duty Engines and Vehicles, CFR 40. Sections 9, 22, 85, 86, 600, 1033, 1036, 1037, 1039,
1042, 1043, 1065, 1066, and 1068
(2016)
Google Scholar ↗
Fontagné et al., 2013 L. Fontagné, J. Fouré, M.P. Ramos
MIRAGE-e: A General Equilibrium Long-Term Path of the World Economy
Centre d'Etudes Prospectives et d'Informations (2013)
Google Scholar ↗
Fujimori et al., 2017 S. Fujimori, T. Hasegawa, T. Masui
AIM/CGE V2.0: basic feature of the model
S. Fujimori, M. Kainuma, T. Masui (Eds.), Post-2020 Climate Action, Springer, Singapore (2017)
Google Scholar ↗
https://www.sciencedirect.com/science/article/pii/S0140988323002086#f0005
37/46
2023/5/12 11:11
Insights from adding transportation sector detail into an economy-wide model: The case of the ADAGE CGE model - ScienceDirect
Girod et al., 2013 B. Girod, D.P. van Vuuren, M. Grahn, A. Kitous, S.H. Kim, P. Kyle
Climate impact of transportation a model comparison
Clim. Chang., 118 (3–4) (2013), pp. 595-608, 10.1007/s10584-012-0663-6 ↗
View in Scopus ↗
Google Scholar ↗
Goettle et al., 2009 R. Goettle, M.S. Ho, D.W. Jorgenson, D.T. Slesnick, P.J. Wilcoxen
Analyzing Environmental Policies with IGEM, an Intertemporal General Equilibrium Model of US
Growth and the Environment: Part 2
United States, Environmental Protection Agency, Washington, DC (2009)
https://www.igem.insightworks.com/docs/247/part2chap1.pdf ↗
Google Scholar ↗
Grahn et al., 2009 M. Grahn, C. Azar, M.I. Williander, J.E. Anderson, S.A. Mueller, T.J. Wallington
Fuel and vehicle technology choices for passenger vehicles in achieving stringent CO2 targets:
connections between transportation and other energy sectors
EnST, 43 (9) (2009), pp. 3365-3371, 10.1021/es802651r ↗
View in Scopus ↗
Google Scholar ↗
Grahn et al., 2013 M. Grahn, E. Klampfl, M.J. Whalen, T.J. Wallington, K. Lindgren
Description of the global energy systems model GET-RC 6.1
Department of Energy and Environment Division of Physical Resource Theory, Chalmers University of Technology,
Gothenburg, Sweden (2013)
https://research.chalmers.se/publication/182030 ↗
Google Scholar ↗
Green et al., 2019 W. Green, S. Paltsev, M. Ben-Akiva, J. Heywood, C. Vaishnav, J. Zhao, B. Reimer, C. Knittel
Insights into future mobility
MIT Energy Initiative (2019)
https://energy.mit.edu/wp-content/uploads/2019/11/Insights-into-Future-Mobility.pdf ↗
Google Scholar ↗
Greene et al., 2018 D. Greene, A. Hossain, J. Hofmann, G. Helfand, R. Beach
Consumer willingness to pay for vehicle attributes: What do we know?
Transp. Res. A Policy Pract., 118 (2018), pp. 258-279, 10.1016/j.tra.2018.09.013 ↗
View PDF
View article
View in Scopus ↗
Google Scholar ↗
Hao et al., 2016 H. Hao, Y. Geng, J. Sarkis
Carbon footprint of global passenger cars: scenarios through 2050
Energy, 101 (2016), pp. 121-131, 10.1016/j.energy.2016.01.089 ↗
View PDF
View article
View in Scopus ↗
Google Scholar ↗
Heaton, 2014 C. Heaton
Modelling Low-carbon Energy System Designs with the ETI ESME Model
United Kingdom, Energy Technologies Institute (2014), 10.5286/UKERC.EDC.000558 ↗
Google Scholar ↗
Himics et al., 2020 M. Himics, G. Listorti, A. Tonini
Simulated economic impacts in applied trade modelling: a comparison of tariff aggregation
approaches
Econ. Model., 87 (2020), pp. 344-357, 10.1016/j.econmod.2019.08.007 ↗
View PDF
View article
View in Scopus ↗
https://www.sciencedirect.com/science/article/pii/S0140988323002086#f0005
Google Scholar ↗
38/46
2023/5/12 11:11
Insights from adding transportation sector detail into an economy-wide model: The case of the ADAGE CGE model - ScienceDirect
Horridge, 2018 M. Horridge
Aggregating CES Elasticities for CGE Models
Australia, Centre of Policy Studies, Victoria University, Melbourne (2018)
https://www.gtap.agecon.purdue.edu/resources/download/9088.pdf ↗
Google Scholar ↗
Huppmann et al., 2019 D. Huppmann, M. Gidden, O. Fricko, P. Kolp, C. Orthofer, M. Pimmer, N. Kushin, A. Vinca, A. Mastrucci,
K. Riahi, V. Krey
The MESSAGE integrated assessment model and the ix modeling platform (ixmp): an open
framework for integrated and cross-cutting analysis of energy, climate, the environment, and
sustainable development
Environ. Model. Softw., 112 (2019), pp. 143-156, 10.1016/j.envsoft.2018.11.012 ↗
View PDF
View article
View in Scopus ↗
Google Scholar ↗
ICES, 2019 ICES
ICES (Inter-temporal Computable Equilibrium System) [Online]
https://www.icesmodel.org/ ↗ (2019)
Google Scholar ↗
Intergovernmental Panel on Climate Change et al., 2014 Intergovernmental Panel on Climate Change, O. Edenhofer, R. PichsMadruga, Y. Sokona, E. Farahani, S. Kadner, K. Seyboth, A. Adler, I. Baum, S. Brunner, P. Eickemeier, B. Kriemann, J.
Savolainen, S. Schlömer, C. von Stechow, T. Zwickel, J.C. Minx, R.S. Sims, F. Creutzig, X. Cruz-Núñez, M. D'Agosto, D.
Dimitriu, M.J. Figueroa Meza, L. Fulton, S. Kobayashi, O. Lah, A. McKinnon, P. Newman, M. Ouyang, J.J. Schauer, D.
Sperling, G. Tiwari
Climate Change 2014: Mitigation of Climate Change
Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge, United
Kingdom and New York, NY, U.S (2014)
Google Scholar ↗
International Energy Agency, 2020 International Energy Agency
Key World Energy Statistics
International Energy Agency (2020)
https://iea.blob.core.windows.net/assets/1b7781df-5c93-492a-acd601fc90388b0f/Key_World_Energy_Statistics_2020.pdf ↗
Google Scholar ↗
International Food Policy Research Institute (IFPRI), 2011 International Food Policy Research Institute (IFPRI)
The MIRAGRODEP Model [Online]
https://www.ifpri.org/publication/miragrodep-model ↗ (2011)
[October 1, 2021]
Google Scholar ↗
Ivanova, 2019 O. Ivanova
EXIOMOD World-wide Dynamic CGE Model
Carbon-CAP (2019)
Google Scholar ↗
Jaccard, 2009 M. Jaccard
Combining top down and bottom up in energy economy models
International Handbook on the Economics of Energy (2009)
Google Scholar ↗
https://www.sciencedirect.com/science/article/pii/S0140988323002086#f0005
39/46
2023/5/12 11:11
Insights from adding transportation sector detail into an economy-wide model: The case of the ADAGE CGE model - ScienceDirect
Jin et al., 2019 D. Jin, S.D. Thube, J. Ziesmer, C. Henning, R. Delzeit
A Baseline Calibration Procedure for CGE Models: An Application for DART
(2019)
Google Scholar ↗
Karkatsoulis et al., 2017 P. Karkatsoulis, P. Siskos, L. Paroussos, P. Capros
Simulating deep CO2 emission reduction in transport in a general equilibrium framework: the
GEM-E3T model
Transp. Res. Part D: Transp. Environ., 55 (2017), pp. 343-358, 10.1016/j.trd.2016.11.026 ↗
View PDF
View article
View in Scopus ↗
Google Scholar ↗
Karplus et al., 2010 V.J. Karplus, S. Paltsev, J.M. Reilly
Prospects for plug-in hybrid electric vehicles in the United States and Japan: a general
equilibrium analysis
Transp. Res. A Policy Pract., 44 (8) (2010), pp. 620-641, 10.1016/j.tra.2010.04.004 ↗
View PDF
View article
View in Scopus ↗
Google Scholar ↗
Karplus et al., 2013a V.J. Karplus, S. Paltsev, M. Babiker, J.M. Reilly
Applying engineering and fleet detail to represent passenger vehicle transport in a computable
general equilibrium model
Econ. Model., 30 (2013), pp. 295-305, 10.1016/j.econmod.2012.08.019 ↗
View PDF
View article
View in Scopus ↗
Google Scholar ↗
Karplus et al., 2013b V.J. Karplus, S. Paltsev, M. Babiker, J.M. Reilly
Should a vehicle fuel economy standard be combined with an economy-wide greenhouse gas
emissions constraint? Implications for energy and climate policy in the United States
Energy Econ., 36 (2013), pp. 322-333, 10.1016/j.eneco.2012.09.001 ↗
View PDF
View article
View in Scopus ↗
Google Scholar ↗
Ko and Britz, 2013 J.-H. Ko, W. Britz
Does regional and sectoral aggregation matter? Sensitivity analysis in the context of an EU-Korea
FTA
16th Annual Conference on Global Economic Analysis, Shanghai, China, 2013 (2013)
https://www.gtap.agecon.purdue.edu/resources/download/6313.pdf ↗
Google Scholar ↗
Kober et al., 2016 T. Kober, P. Summerton, H. Pollitt, U. Chewpreecha, X. Ren, W. Wills, C. Octaviano, J. McFarland, R. Beach, Y.
Cai, S. Calderon, K. Fisher-Vanden, A.M.L. Rodriguez
Macroeconomic impacts of climate change mitigation in Latin America: a cross-model
comparison
Energy Econ., 56 (2016), pp. 625-636, 10.1016/j.eneco.2016.02.002 ↗
View PDF
View article
View in Scopus ↗
Google Scholar ↗
Kyle and Kim, 2011 P. Kyle, S.H. Kim
Long-term implications of alternative light-duty vehicle technologies for global greenhouse gas
emissions and primary energy demands
Energy Policy, 39 (5) (2011), pp. 3012-3024, 10.1016/j.enpol.2011.03.016 ↗
View PDF
View article
View in Scopus ↗
Google Scholar ↗
Lindholt, 2019 L. Lindholt
https://www.sciencedirect.com/science/article/pii/S0140988323002086#f0005
40/46
2023/5/12 11:11
Insights from adding transportation sector detail into an economy-wide model: The case of the ADAGE CGE model - ScienceDirect
Energy in the SNOW Model: Description of Production and Consumption of Energy in Norway in
the Base Year 2013
Statistisk Sentralbyrå, Norway (2019)
Google Scholar ↗
Loulou et al., 2005 R. Loulou, U. Remne, A. Kanudia, A. Lehtila, G. Goldstein
Documentation for the TIMES Model - PART I
(2005), pp. 1-78
View in Scopus ↗
Google Scholar ↗
Lucena et al., 2018 A.F.P. Lucena, M. Hejazi, E. Vasquez-Arroyo, S. Turner, A.C. Köberle, K. Daenzer, P.R.R. Rochedo, T. Kober, Y.
Cai, R.H. Beach, D. Gernaat, D.P. van Vuuren, B. van der Zwaan
Interactions between climate change mitigation and adaptation: the case of hydropower in Brazil
Energy, 164 (2018), pp. 1161-1177, 10.1016/j.energy.2018.09.005 ↗
View PDF
View article
View in Scopus ↗
Google Scholar ↗
Luderer et al., 2015 G. Luderer, M. Leimbach, N. Bauer, E. Kriegler, L. Baumstark, C. Bertram, A. Giannousakis, J. Hilaire, D.
Klein, A. Levesque, I. Mouratiadou
Description of the REMIND Model (Version 1.6)
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2697070 ↗ (2015)
Google Scholar ↗
Marten et al., 2021 A. Marten, A. Schreiber, A. Wolverton
SAGE Model Documentation (2.0.1)
U.S. Environmental Protection Agency, Washington, D.C., USA (2021)
https://www.epa.gov/sites/default/files/2020-12/documents/sage-model-documentation-version-2.0.0_0.pdf ↗
Google Scholar ↗
Mathiesen et al., 2015 B.V. Mathiesen, H. Lund, D. Connolly, H. Wenzel, P.A. Østergaard, B. Möller, S. Nielsen, I. Ridjan, P.
Karnøe, K. Sperling, F.K. Hvelplund
Smart energy systems for coherent 100% renewable energy and transport solutions
Appl. Energy, 145 (2015), pp. 139-154, 10.1016/j.apenergy.2015.01.075 ↗
View PDF
View article
View in Scopus ↗
Google Scholar ↗
Meyer et al., 2007 I. Meyer, M. Leimbach, C.C. Jaeger
International passenger transport and climate change: a sector analysis in car demand and
associated emissions from 2000 to 2050
Energy Policy, 35 (12) (2007), pp. 6332-6345
View PDF
View article
View in Scopus ↗
Google Scholar ↗
MIT Joint Program on the Science and Policy of Global Change, 2023 MIT Joint Program on the Science and Policy of Global
Change
EPPA Model Structure [Online]
https://globalchange.mit.edu/research/research-tools/eppa ↗ (2023)
[October 1, 2021]
Google Scholar ↗
Muratori et al., 2020 M. Muratori, P. Jadun, B. Bush, D. Bielen, L. Vimmerstedt, J. Gonder, C. Gearhart, D. Arent
Future integrated mobility-energy systems: a modeling perspective
Renew. Sust. Energ. Rev., 119 (2020), 10.1016/j.rser.2019.109541 ↗
Google Scholar ↗
Nalley et al., 2019 S. Nalley, A. LaRose, J. Diefenderfer, J. Staub, J. Turnure, L. Westfall
https://www.sciencedirect.com/science/article/pii/S0140988323002086#f0005
41/46
2023/5/12 11:11
Insights from adding transportation sector detail into an economy-wide model: The case of the ADAGE CGE model - ScienceDirect
The National Energy Modeling System: An overview of 2018
U.S. Department of Energy, Washington, DC (2019)
https://www.eia.gov/outlooks/aeo/nems/overview/pdf/0581(2018).pdf ↗
Google Scholar ↗
Narayanan and Walmsley, 2008 G.B. Narayanan, T.L. Walmsley
Global Trade, Assistance, and Production: The GTAP 7 Data Base
Center for Global Trade Analysis, Purdue University, West Lafayette, IN (2008)
http://www.gtap.agecon.purdue.edu/databases/v7/v7_doco.asp ↗
Google Scholar ↗
National Highway Traffic Safety Administration, 2010 National Highway Traffic Safety Administration
Light-Duty Vehicle Greenhouse Gas Emission Standards and Corporate Average Fuel Economy
Standards; Final Rule. 75 FR, Section 25323
(2010)
Google Scholar ↗
National Renewable Energy Laboratory, 2020 National Renewable Energy Laboratory
2020 Annual Technology Baseline (ATB) Cost and Performance Data for Electricity Generation
Technologies [Dataset]
(2020), 10.25984/1807473 ↗
Google Scholar ↗
Niamir et al., 2020 L. Niamir, O. Ivanova, T. Filatova
Economy-wide impacts of behavioral climate change mitigation: linking agent-based and
computable general equilibrium models
Environ. Model. Softw., 134 (2020), 10.1016/j.envsoft.2020.104839 ↗
Google Scholar ↗
Nykvist and Nilsson, 2015 B. Nykvist, M. Nilsson
Rapidly falling costs of battery packs for electric vehicles
Nat. Clim. Chang., 5 (4) (2015), pp. 329-332, 10.1038/nclimate2564 ↗
View in Scopus ↗
Google Scholar ↗
Pacific Northwest National Laboratory, 2021 Pacific Northwest National Laboratory
Global Change Assessment Model v5.3 Documentation: GCAM Model Overview
Joint Global Change Research Institute, University of Maryland (2021)
http://jgcri.github.io/gcam-doc/overview.html ↗
Google Scholar ↗
Paltsev et al., 2004 S. Paltsev, L. Viguier, M. Babiker, J. Reilly, K.H. Tay
Disaggregating Household Transport in the MIT-EPPA Model
Joint Program on the Science and Policy of Global Change, MIT (2004)
http://web.mit.edu/globalchange/www/MITJPSPGC_TechNote5.pdf ↗
Google Scholar ↗
Paltsev et al., 2005 S. Paltsev, J.M. Reilly, H.D. Jacoby, R.S. Eckaus, J. McFarland, M. Sarofim, M. Asadoorian, M. Babiker
The MIT Emissions Prediction and Policy Analysis (EPPA) Model: Version 4
(2005)
https://globalchange.mit.edu/publication/14578 ↗
Google Scholar ↗
Philippidis et al., 2018 G. Philippidis, H. Bartelings, J. Helming, R. M'Barek, T. Ronzon, E. Smeets, H.A. van Meijl, L. Shutes
https://www.sciencedirect.com/science/article/pii/S0140988323002086#f0005
42/46
2023/5/12 11:11
Insights from adding transportation sector detail into an economy-wide model: The case of the ADAGE CGE model - ScienceDirect
The MAGNET Model Framework for Assessing Policy Coherence and SDGs: Application to the
Bioeconomy
Joint Research Centre (Seville site) (2018)
https://ideas.repec.org/p/ipt/iptwpa/jrc111508.html ↗
Google Scholar ↗
Pietzcker et al., 2014 R.C. Pietzcker, T. Longden, W. Chen, S. Fu, E. Kriegler, P. Kyle, G. Luderer
Long-term transport energy demand and climate policy: alternative visions on transport
decarbonization in energy-economy models
Energy, 64 (2014), pp. 95-108, 10.1016/j.energy.2013.08.059 ↗
View PDF
View article
View in Scopus ↗
Google Scholar ↗
Sands, 2021 R. Sands
Tracking calories in CGE models: from fork to farm
24th Annual Conference on Global Economic Analysis, Virtual, 2021 (2021)
https://www.gtap.agecon.purdue.edu/resources/res_display.asp?RecordID=6381 ↗
Google Scholar ↗
Sands et al., 2014 R. Sands, C. Jones, E.P. Marshall
Global Drivers of Agricultural Demand and Supply
U.S. Department of Agriculture, Economic Research Service. Department of Agriculture, Economic Research Service (2014)
Google Scholar ↗
Sassi et al., 2010 O. Sassi, R. Crassous, J.C. Hourcade, V. Gitz, H. Waisman, C. Guivarch
IMACLIM-R: a modelling framework to simulate sustainable development pathways
Int. J. Glob. Env. Issues, 10 (1/2) (2010), 10.1504/ijgenvi.2010.030566 ↗
Google Scholar ↗
Schäfer and Jacoby, 2005 A. Schäfer, H.D. Jacoby
Technology detail in a multisector CGE model: transport under climate policy
Energy Econ., 27 (1) (2005), pp. 1-24, 10.1016/j.eneco.2004.10.005 ↗
View PDF
View article
View in Scopus ↗
Google Scholar ↗
Sentinel, 2023 Sentinel
WEGDYN [Online]
https://sentinel.energy/model/wegdyn/ ↗ (2023)
[October 1, 2021]
Google Scholar ↗
Tavasszy et al., 2011 L.A. Tavasszy, M.J.P.M. Thissen, J. Oosterhaven
Challenges in the application of spatial computable general equilibrium models for transport
appraisal
Res. Transp. Econ., 31 (1) (2011), pp. 12-18, 10.1016/j.retrec.2010.11.003 ↗
View PDF
View article
View in Scopus ↗
Google Scholar ↗
Thissen, 2019 M. Thissen
EU Economic Modelling System: Assessment of the European Institute of Innovation and
Technology (EIT) Investments in Innovation and Human Capital
Economics and Econometrics Research Institute (EERI), Brussels (2019)
https://www.econstor.eu/handle/10419/213555 ↗
Google Scholar ↗
https://www.sciencedirect.com/science/article/pii/S0140988323002086#f0005
43/46
2023/5/12 11:11
Insights from adding transportation sector detail into an economy-wide model: The case of the ADAGE CGE model - ScienceDirect
U.S. Department of Energy, Energy Information Administration (EIA), 2018 U.S. Department of Energy, Energy Information
Administration (EIA)
Annual Energy Outlook 2018
U.S. Energy Information Agency, U.S. Department of Energy, Washington, DC (2018)
http://www.eia.doe.gov/oiaf/aeo/ ↗
Google Scholar ↗
U.S. DOE, 2015 U.S. DOE
Record 15015: Fuel Cell System Cost - 2015
U.S. Department of Energy Hydrogen and Fuel Cells Program (2015)
https://www.hydrogen.energy.gov/pdfs/15015_fuel_cell_system_cost_2015.pdf ↗
Google Scholar ↗
UCL Wiki, 2014 UCL Wiki
Model Concept, Solver and Details - IMACLIM [Online]
https://wiki.ucl.ac.uk/display/ADVIAM/Model%20concept,%20solver%20and%20details%20-%20IMACLIM ↗ (2014)
Google Scholar ↗
van der Mensbrugghe, 2018 D. van der Mensbrugghe
The Environmental Impact and Sustainability Applied General Equilibrium (ENVISAGE) Model,
Version 9.0
(2018)
https://mygeohub.org/groups/gtap/File:ENVISAGE9_Documentation.pdf ↗
Google Scholar ↗
van Ruijven et al., 2016 B.J. van Ruijven, K. Daenzer, K. Fisher-Vanden, T. Kober, S. Paltsev, R.H. Beach, S.L. Calderon, K. Calvin,
M. Labriet, A. Kitous, A.F.P. Lucena, D.P. van Vuuren
Baseline projections for Latin America: base-year assumptions, key drivers and greenhouse
emissions
Energy Econ., 56 (2016), pp. 499-512, 10.1016/j.eneco.2015.02.003 ↗
View PDF
View article
View in Scopus ↗
Google Scholar ↗
Wade et al., 2019 C.M. Wade, J.S. Baker, G. Latta, S.B. Ohrel
Evaluating potential sources of aggregation bias with a structural optimization model of the U.S.
forest sector
J. For. Econ., 34 (3–4) (2019), pp. 337-366, 10.1561/112.00000503 ↗
View in Scopus ↗
Google Scholar ↗
Waisman et al., 2012 H. Waisman, C. Guivarch, F. Grazi, J.C. Hourcade
The Imaclim-R model: infrastructures, technical inertia and the costs of low carbon futures under
imperfect foresight
Clim. Chang., 114 (1) (2012), pp. 101-120, 10.1007/s10584-011-0387-z ↗
View in Scopus ↗
Google Scholar ↗
Winning et al., 2017 M. Winning, A. Calzadilla, R. Bleischwitz, V. Nechifor
A GTAP-based model for analysing resource efficiency and the circular economy
20th Annual Conference on Global Economic Analysis, West Lafayette, IN, USA, 2017 (2017)
Google Scholar ↗
Woltjer et al., 2014 G.B. Woltjer, M. Kuiper, A. Kavallari, H. van Meijl, J.P. Powell, M.M. Rutten, L.J. Shutes, A.A. Tabeau
The MAGNET model: Module description
LEI Wageningen UR (2014)
https://www.sciencedirect.com/science/article/pii/S0140988323002086#f0005
44/46
2023/5/12 11:11
Insights from adding transportation sector detail into an economy-wide model: The case of the ADAGE CGE model - ScienceDirect
Google Scholar ↗
World Bank, 2012 World Bank
Energy Intensity of Industrial Sector [dataset]
World Bank (2012)
https://tcdata360.worldbank.org/indicators/ener.int.ind?country=BRA&indicator=3807&viz=line_chart&years=1990,2012 ↗
Google Scholar ↗
Yeh et al., 2017 S. Yeh, G.S. Mishra, L. Fulton, P. Kyle, D.L. McCollum, J. Miller, P. Cazzola, J. Teter
Detailed assessment of global transport-energy models' structures and projections
Transp. Res. Part D: Transp. Environ., 55 (2017), pp. 294-309, 10.1016/j.trd.2016.11.001 ↗
View PDF
View article
View in Scopus ↗
Google Scholar ↗
Yuan et al., 2019 M. Yuan, S. Rausch, J. Caron, S. Paltsev, J. Reilly
The MIT US Regional Energy Policy (USREP) Model: The Base Model and Revisions
MIT Joint Program on the Science and Policy of Global Change (2019)
https://globalchange.mit.edu/publication/17331 ↗
Google Scholar ↗
Zhang et al., 2018 D. Zhang, J. Caron, N. Winchester
Sectoral aggregation error in the accounting of energy and emissions embodied in trade and
consumption
J. Ind. Ecol., 23 (2) (2018), pp. 402-411, 10.1111/jiec.12734 ↗
View in Scopus ↗
Google Scholar ↗
Zhu et al., 2018 Y. Zhu, M. Ghosh, D. Luo, N. Macaluso, J. Rattray
Revenue recycling and cost effective ghg abatement: an exploratory analysis using a global multisector multi-region CGE model
Clim. Change Econom., 09 (01) (2018), 10.1142/s2010007818400092 ↗
Google Scholar ↗
Cited by (0)
Glossary
Aggregation bias
The expected difference between behavior when individual components (e.g., technologies) are
modeled as a group versus when they are modeled individually.
Armington aggregation
The aggregation of similar goods (e.g., a commodity produced in different locations) into a single
good.
Comparative-static
Analyzing the impact of a change in the parameters or variables of a model by comparing the
model outcome before and after a change.
Computable general equilibrium (CGE) model
Economic models that account for economy wide factor utilization, production technologies, and
end-uses in a closed accounting framework that determines prices and conserves economic value.
Constant elasticity of substitution (CES)
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A functional form used for modeling production and consumption behavior that posits a constant
percent change in the quantity ratios of two inputs relative to price ratios.
Dynamic
Economic models that include change over time.
Gross domestic product (GDP)
The monetary value of total income or expenditure (including savings and investment) within a
country.
Intertemporal
Dynamic models that include responsiveness to future modeled outcomes.
Leontief production function
A production function with no substitution between inputs.
Recursive-dynamic
CGE models that assume behavior depends only on current and past states of the economy and
can be solved sequentially.
Sectoral heterogeneity
Differences in input requirements and substitution responses across sectors.
1
We focus here on national and global energy-focused models incorporating transportation modules. However, there are a broader array of
CGE models that have been applied to analyses of transportation issues, including models that are primarily focused on urban, regional,
economic development and related questions rather than energy and environmental economics (Robson, Wijayaratna, and Dixit, 2018).
2
In this paper, costs and prices are presented in 2010 dollars.
3
The crude oil price in the LOP case starts at $28/barrel in 2020 and rises slowly to $46/barrel in 2050. The crude oil price in the HOP case
starts at $109/barrel in 2020 and rises to $205/barrel by 2050. We continued using the 2018 AEO projections for the LOP and HOP scenarios
as well with the same rationale. Neither the AEO projections nor the ADAGE model includes the impact of countries' commitments under
the Paris Agreement that are not already enacted in law.
View Abstract
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