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STEPS Transportation Transition
Scenarios for California
Christopher Yang, Marshall Miller, Lew Fulton, Joan
Ogden, Rosa Dominguez-Faus, Dominique Meroux
STEPS Symposium
June 2, 2016
www.steps.ucdavis.edu
NextSTEPS (Sustainable Transportation Energy Pathways)
STEPS Decarbonization Scenarios for Transportation
• Critical Transition Dynamics 2015-2030
• Develop scenarios for transportation to analyze future vehicle
mixes, fuel usage, emissions and costs
• Integrate ongoing STEPS research on vehicles and fuels
• Focus on the cost and emissions impacts of a transition to decarbonized
transportation system (vehicles and fuels)
• Analyze 2010-2050 with particular focus on 2015-2030
• Explore detailed vehicle/fuel scenarios across many transport sectors
• Project goals
• Develop scenario modeling framework
• Produce realistic scenarios that help contribute to meeting climate
change goals in transportation
• Assess technology/fuel/resource mix and emissions
• Assess incremental costs (and potential subsidies required)
• Scenarios enable “what-if” analyses and improve understanding of
sensitivities of the system to inputs
UnspecifiedAvia on
IntrastateAvia on
OtherAvia on
Light-dutyCars
Light-dutyTrucks&SUVs
Heavy-dutyTrucks,Buses
Motorcycles
Water-bornePort
WaterborneTransit
WaterborneHarborCra
Off-roadIndustrial
Off-roadOilDrilling
Interna onalAvia on
InterstateAvia on
Interna onalMarine
Cars
Light Trucks
HDV
Cars
Ex: Aviation
Ex:
Marine
Decarbonization Scenarios for Transportation
• Analyze reference (BAU) and decarbonization (GHG) scenarios
• Look across transportation sectors
– Light-duty, medium and heavy-duty/medium-duty trucks initially
– Additional sectors to be included later
• Start with focus on California to build up modeling capabilities but
plan to develop US scenarios
• Similar approach (technology specifications, modeling framework)
• Differences (additional data collection, infrastructure and resource
availability and cost)
2010 CA
Transportation
Emissions
Transition Scenario Modeling Framework
• Spreadsheet-based model
– Specify vehicle technologies (sales mix, fuel consumption, cost)
– Specify fuel supply (production/delivery pathways, carbon intensity, cost)
Vehiclestockturnovermodelforeachvehicletype/technology
FuelInfrastructureaccoun ngmodelforeachfueltype/
pathway
VehicleInputData FuelsInputData
FuelConsump on
ModelOutputsVehicleCosts
InfrastructureCostsIncrementalCostsGHGemissions
Fuelconsump onTotalResourceUsage
Not yet
completed
CA Scenarios Progress and Results
• Work is ongoing and we have completed the light-duty vehicle
sector and several heavy-duty and medium-duty truck applications
– California data and scenarios
– Stock turnover model based upon VISION model
– Vehicle component cost model
• Currently assume trajectory for component costs, but will incorporate learning
for batteries, fuel cells and other key components as a function of adoption
– Simple representation of fuel pathways and fuel costs
• More detailed infrastructure (resource supply, production, transport, refueling)
representation will be developed
• Lots of assumptions about fuel blends, carbon intensity, and costs across BAU
and GHG scenarios
The results shown in the following slides are preliminary scenarios
examples from this first stage scenario model
LDVs scenarios compared
• Reference scenario (BAU): ZEV compliant scenario ~16% of
vehicles in 2025 are ZEVs or TZEVs
– No additional growth in adoption after that
• Low Carbon scenario (GHG): Aggressive uptake of ZEVs by 2030:
46% of cars/light trucks sold in 2030 are EVs and PHEVs, and
~90% in 2040
– Scenarios are identical to 2015
0
5,000
10,000
15,000
20,000
25,000
30,000
2010 2015 2020 2025 2030 2035 2040 2045 2050
LDVFleet(ThousandsofVehicles)
LDVFleetMix
FCV
BEV
PHEV
HEV
CNG
Conv+FFV
0
5,000
10,000
15,000
20,000
25,000
30,000
2010 2015 2020 2025 2030 2035 2040 2045 2050
LDVFleet(ThousandsofVehicles)
LDVFleetMix
FCV
BEV
PHEV
HEV
CNG
Conv+FFV
BAU GHG
Reference (BAU) HD and MD Trucks scenarios
• Conservative adoption of alternative vehicle technologies in LH and SH.
• CNG is adopted fairly substantially in MD delivery
0
50
100
150
200
250
2010 2015 2020 2025 2030 2035 2040 2045 2050
LHFleet(ThousandsofVehicles)
Long-HaulFleetMix
BEV
FuelCell
CNGSI
LNGSI
LNGCI
Hybrid
Diesel
0
20
40
60
80
100
120
140
2010 2015 2020 2025 2030 2035 2040 2045 2050
SHFleet(ThousandsofVehicles)
Short-HaulFleetMix
BEV200
BEV100
FuelCell
CNGSi
LNGSi
LNGCI
Hybrid
Diesel
0
50
100
150
200
250
300
350
400
2010 2015 2020 2025 2030 2035 2040 2045 2050
MDDeliveryFleet(ThousandsofVehicles)
Medium-DutyDeliveryFleetMix
BEV200
BEV100
FuelCell
CNG
Hybrid
Gasoline
Diesel
Decarbonized (GHG) HD and MD scenarios
• Sales of 50% FCVs in LH and SH by 2050. B50 Diesel blend.
• MD has substantial CNG, Fuel Cell and BEVs by 2050
0
50
100
150
200
250
2010 2015 2020 2025 2030 2035 2040 2045 2050
LHFleet(ThousandsofVehicles)
Long-HaulFleetMix
BEV
FuelCell
CNGSI
LNGSI
LNGCI
Hybrid
Diesel
0
20
40
60
80
100
120
140
2010 2015 2020 2025 2030 2035 2040 2045 2050
SHFleet(ThousandsofVehicles)
Short-HaulFleetMix
BEV200
BEV100
FuelCell
CNGSi
LNGSi
LNGCI
Hybrid
Diesel
0
50
100
150
200
250
300
350
400
2010 2015 2020 2025 2030 2035 2040 2045 2050
MDDeliveryFleet(ThousandsofVehicles)
Medium-DutyDeliveryFleetMix
BEV200
BEV100
FuelCell
CNG
Hybrid
Gasoline
Diesel
LDV Results
• BAU - has significant increase
in fuel economy so fuel
consumption drops by 21%
(2030) and 33% (2050)
• GHG - even larger reduction
in fuel consumption, 33% in
2030 and 57% in 2050
0
2000
4000
6000
8000
10000
12000
14000
2010 2015 2030 2050 2030 2050
BAU GHG
Fu
el C
on
su
mp
tio
n (
Milli
on
GG
E)
H2
Electricity
CNG
Biodiesel
Diesel
Ethanol
Gasoline
-
10
20
30
40
50
60
70
80
2010 2015 2020 2025 2030 2035 2040 2045 2050
LD
V F
ue
l E
co
no
my
(M
PG
GE
)
BAU GHG
-
20,000
40,000
60,000
80,000
100,000
120,000
140,000
160,000
2010 2020 2030 2040 2050
An
nu
al
LD
V G
HG
Em
iss
ion
s
(ktC
O2/y
r)
BAU
HD and MD Results
• Fuel economy improvements lead to substantial reduction in fuel
consumption: 25% (2030) and 20% (2050) in BAU, 26% (2030) to
32% (2050) in GHG scenario
0
500
1000
1500
2000
2500
3000
3500
4000
2010 2015 2030 2050 2030 2050
BAU GHG
Fu
el C
on
su
mp
tio
n (
Milli
on
GG
E)
H2
Electricity
CNG
Biodiesel
Diesel
Ethanol
Gasoline
-
5,000
10,000
15,000
20,000
25,000
30,000
35,000
40,000
45,000
2010 2020 2030 2040 2050
An
nu
al G
HG
Em
issio
ns
(k
tCO
2/y
r)
BAU
GHG emissions comparison
• Greater emissions reduction from LDVs due to greater adoption of
advanced and zero-emission vehicles
0
20
40
60
80
100
120
140
160
180
200
2010 2015 2020 2025 2030 2035 2040 2045 2050
GH
G E
mis
sio
ns (
MtC
O2
e)
HDV Reductions LDV Reductions HDV Emissions LDV Emissions BAU GHG
2030 2050
LDV HD+MD Total LDV HD+MD Total
reduction from 2010 levels
BAU 22.4% 26.1% 23.2% 35.5% 22.6% 32.7%
GHG 33.7% 28.7% 32.6% 73.3% 55.0% 69.3%
reduction below BAU GHG 14.5% 3.6% 12.2% 58.6% 41.9% 54.4%
LDV Cost Comparison
• Annual expenditures- Fuel: $26 billion Vehicles: $46 billion in 2015
• Incremental vehicle cost: up to $4 billion/yr
• Fuel savings grows over time
• Fuel savings balance incremental vehicle cost in 2030
• Total incremental cost
– to 2030: $13 billion
• Large savings after 2035
-15,000
-10,000
-5,000
-
5,000
10,000
15,000
2015 2020 2025 2030 2035 2040 2045 2050
An
nu
al
Co
sts
($
Mil
lio
ns
)
Fuel
Vehicle
F+V
Cumulative F+V
PV Cumulative
-15,000
-10,000
-5,000
-
5,000
10,000
15,000
2015 2020 2025 2030 2035 2040 2045 2050
An
nu
al
Co
sts
($
Mil
lio
ns
)
Fuel
Vehicle
F+V
HDV + MDV Cost Comparison
• Annual expenditures- Fuel: $7 billion Vehicles: $3 billion in 2015
• Incremental vehicle cost: up to $300 million/yr
• Fuel savings balance incremental vehicle cost in 2037
• Total incremental cost
– to 2037: $1.7 billion
• Large savings after 2040
-2,000
-1,500
-1,000
-500
-
500
1,000
1,500
2,000
2015 2020 2025 2030 2035 2040 2045 2050
An
nu
al
Co
sts
($
Mil
lio
ns)
Fuel
Vehicle
F+V
Cumulative F+V
PV Cumulative
-2,000
-1,500
-1,000
-500
-
500
1,000
1,500
2,000
2015 2020 2025 2030 2035 2040 2045 2050
An
nu
al
Co
sts
($
Mil
lio
ns)
Fuel
Vehicle
F+V
Abatement Cost Comparison
• The cost of CO2 reduction ($/tonne CO2) is comparable between
light-duty vehicles and trucks
– LDVs have higher emissions reduction potential
– Greater total costs ($/tonne x tonnes reduced)
-400
-200
0
200
400
600
800
1000
1200
1400
1600
0 10 20 30 40 50 60 CO
2 R
ed
uc
tio
n C
os
t ($
/tC
O2
e)
Annual Emissions Reduction (MtCO2e)
'LDV'
'HDV + MDV'
20502050
2040
2040
20302030
2020
-400
-200
-
200
400
600
800
1,000
1,200
1,400
1,600
2015 2020 2025 2030 2035 2040 2045 2050
GH
G M
itig
ati
on
Co
st
($/t
CO
2e
)
Annual LDV
Cumulative LDV
Annual HD/MD
Cumulative HD/MD
Initial Findings
• We built a spreadsheet framework for our transition scenarios modeling
and incorporated largest/most important transportation sectors
– LDVs
– Most HDVs (Long-Haul, Short-Haul and MD Delivery)
• We developed two preliminary scenarios, a Reference and GHG
reduction scenario to analyze emissions, fuel and cost impacts of the
transition to a low-carbon transport system
• LDVs can achieve a 73% GHG reduction from 2010 levels by 2050,
ultimately at negative cost of abatement
– substantial incremental cost in the medium-term ($13 billion by 2030)
• HDVs and MDVs achieve 55% reduction from 2010 levels by 2050, also
with negative cost of abatement.
– Incremental cost of $1.7 billion by 2038
• Abatement costs ($/tonne CO2) are high initially (at low levels of GHG
reduction), but decline substantially, becoming negative, as GHG
reduction quantity increases
Next Steps
• Add additional transportation sectors/segments
– Other truck sectors (vocational, light-heavy duty)
– Bus, Rail, Air, Marine, Off-road
• Improve representation of fuel resource supply, production and
infrastructure
• Continue to refine cost and vehicle performance assumptions
• Explore other scenarios of interest