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

-

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40,000

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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

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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

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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

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500

1,000

1,500

2,000

2015 2020 2025 2030 2035 2040 2045 2050

An

nu

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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

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1200

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1600

0 10 20 30 40 50 60 CO

2 R

ed

uc

tio

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os

t ($

/tC

O2

e)

Annual Emissions Reduction (MtCO2e)

'LDV'

'HDV + MDV'

20502050

2040

2040

20302030

2020

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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