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Transportation Energy Demand andEmissions in China’s Provinces to 2030

Paul Natsuo Kishimoto <pnk@MIT.EDU>,ZHANG Da, Valerie J. Karplus

37th IAEE Int’l Conference16 June 2014 NYC, US

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Outline

ReviewResearch questionsMethodology for regional & modal projection

Baseline projections

Policy analysis exampleFuel + sectoral output taxesBroader economic impactTransport sector impacts

Extensions

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Review

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ReviewPaltsev et al. (2004) — household transport within acomputable general equilibrium framework (MIT EPPA model).

É Karplus et al. (2013) — detailed methodology forhousehold vehicle transport (HVT), application to the U.S.with detail of advanced powertrain technologies.

É Kishimoto et al. (2012) — application to China (singleregion); Karplus et al. (2012) — global fuel economystandards.

É Schäfer et al. (2009) — Transportation in aClimate-Constrained World, technology analysis & modelcoupling.

Zhang et al. (2013) — China Regional Energy Model (C-REM).É Kishimoto et al. (2013) — disaggregation of commercial

freight & passenger transport in static (2007) C-REM.É International Energy Workshop, 04 June 2014 —

(general) recursive-dynamic projections to 2030.

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Research questions

É How do transport activity and emissions evolve acrossChina’s regions to 2030, within broader economic trends?

É What impacts do transport-focused policies have acrosssector sectors and provinces?

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China Regional Energy Model overview

Features:É 14 sectorsÉ Interprovincial migration

impacts on labor marketthrough 2020 (X. Luo)

É Pollutant representation(C. Waugh)

É Health effects module(K.-M. Nam)

É 30 provinces in ChinaÉ Rest of world aggregated

to four regions

Region r

30 CN regions + 4 international regions

Privatehouseholds

Production(14 sectors)

Primary factors — capital, labour, resources

Income

Goods & services

Expenditures

Intermediatedemand &payments

Data sources:É GTAP Data base v8 (Narayanan G. et al., 2012) for

international economic data; energy, emissions.É National Bureau of Statistics of China (2008) — ChinaEnergy Statistical Yearbook; China Input-Output Tables.

É Aggregation, balancing and adjustment for consistency ofthe Social Accounting Matrix (SAM) and energy flows.

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Chinese provinces & regions西西西 West — 中中中 Central — 东东东 East

XJNM

HL

JL

LN

(XZ)

QH GSNX

SN

SXHE

BJTJ

SD

HA

SCCQ

HBAH

JS

YN

GZHN JX

ZJ

SH

GX GD

FJ

(MC)(HC)

HI

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Transportation subsectors

Code TRN sub-sector Physical unit1

FO Non-road (“other”) freight tonne·kmFR Road freight tonne·kmPO Non-road commercial passenger passenger·kmPR Road commercial passenger passenger·kmHVT Household (private) vehicle transport passenger·km

1Model outputs in economic value terms (constant 2007RMB) are indexed to physical accounts.

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FO, FR, PO, PR: sectoral production

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HVT: own-supplied transportHousehold

consumption

Otherconsumption

Passengertransport (P)

Commercialpassenger

Household vehicletransport (HVT)

New HVT

Fuel

OIL

PowertraincapitalMAN

VehiclecapitalMAN

Services

SER

Vintage HVT

. . . .

σHT1 = 0.5

σHT2 = 0.2

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Recursive-dynamic projectionMethodology of MIT EPPA (Paltsev et al., 2005) and USREP (Rauschet al., 2011) models adopted

1. Solve the static model in the 2007 base year.2. Update exogenous quantities using dynamic parameters:

É Population & labour productivity.É Autonomous energy efficiency improvement (Schmalensee

et al., 1998)É Resource depletion & exploration.É Capital accumulation & deprecation [& vintaging].

3. Re-solve with new conditions in each subsequent periods:2010, 2015, 2020, 2025, 2030.

(Contrast with forward-looking models in which agents performintertemporal utility/profit maximization over present and future periodconsumption, with all periods solved simultaneously; see Babiker et al.,2009)

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Baseline projections

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Gross regional productGrowth rates differ across regions over time; disparity in per-capitaincomes persists

2010 ’20 ’300

2

4

6

8

·103

[10

9U

SD

@2

00

7]

Regional GDP

2010 ’20 ’300

2

4

6

8

Growth (AAGR) [%]

WestCentralEast

2010 ’20 ’300

5

10

15

20

25

30

35

[10

3U

SD

@2

00

7 /ca

pit

a]

Provincial GDP

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Gross regional productGDP and emissions per capita, all provinces, 2007–2030

0 5 10 15 20 25 30 350

5

10

15

20

·102

GDP per capita [103 USD@2007]

CO

2em

issi

ons

per

cap

ita

AH BJ

CQ FJ

GD GS

GX GZ

HA HB

HE HI

HL HN

JL JS

JX LN

NM NX

QH SC

SD SH

SN SX

TJ XJ

YN ZJ

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CO2 emissions, all sectors

0

1

2

3 ·103西 HH

WTRTRNSEROMNOILMANGASELEEISCRUCONCOLAGR

0

5

10

15 ·103

CO

2排放

[Gt /a

]

中

’07 2010 ’15 ’20 ’25 ’300

2

4

6

8 ·103东

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Total & per capita activity, by mode

0

5

10

·102

[t·k

m/a

]or

[pas

s.·k

m/a

]

西

0

0.2

0.4

0.6

0.8

1·104

中

0

0.5

1

1.5

2·104

东 FRFOPRPOHVT

2010 ’20 ’300

5

10

[10

3km

/a]

2010 ’20 ’300

5

10

2010 ’20 ’300

5

10

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HVT stock and per-capita ownership

2010 ’20 ’300

50

100

150

200

250

Year

Total vehicles [106]Western ChinaCentral ChinaEastern China

0 5 10 15 200

5 · 10−2

0.1

0.15

0.2

0.25

0.3

GDP [103 USD @ 2007/capita]

Vehicles per capita

西

中

东

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Egy. demand & CO2 emissions, by mode

0

0.5

1

Ener

gy

[10

0M

tce /

a] 西

0

2

4

6 中

0

2

4

6 东HVTPOPRFOFR

2010 ’20 ’300

1

2

CO

2[1

00

Mt /a

]

2010 ’20 ’300

5

10

15

2010 ’20 ’300

5

10

15

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Policy analysis example

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Analysis of an example policy

Model a transport-sector specific policy2—10% ad-valoremtaxes on:1. OIL input to HVT.2. Output of road passenger (PR) sector.3. Output of road freight (FR) mode.

É Lump-sum remittance to households.É Policy begins in 2015 and continues through end of model

forecast period (i.e. 2030).

2In a GE framework, transport sectors will also respond to changes indemand & (energy) input prices caused by economy-wide policy, or bytargeted policy in non-transport sectors.

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Policy impact: aggregate consumptionSome differences across provinces, but small overall impact on thismeasure of welfare due to remittance

2010 ’20 ’30

0.995

1.000WestCenterEast

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Policy impact: transport activity changeIndexed to baseline projection — 1.0 = baseline value in given year

2010 ’20 ’300.8

0.85

0.9

0.95

1

1.05西

2010 ’20 ’30

中

FR POFO HVTPR

2010 ’20 ’30

东

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CO2 emissions of transport sectorsPolicy impact: some reduction in energy intensity; fuel switching

2010 ’20 ’300.8

0.85

0.9

0.95

1

1.05西

2010 ’20 ’30

中

FR PR HVTFO PO

2010 ’20 ’30

东

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Policy impact: vehicle ownershipThin trajectories = policy case

0 2 4 6 8 10 12 14 16 18 200

5 · 10−2

0.1

0.15

0.2

0.25

0.3

GDP [103 USD @ 2007/capita]

Vehic

les

per

cap

ita

西 中 东

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Extensions

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GDP & vehicles per capita — the dataSichuan prefectures 1998–2011; prov. average (grey)

0 1 2 3 4 5 6 7 8 9

·104

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

GDP [RMB·person−1]

Beijing

Tianjin

Shanghai

Chongqing

Sichuan

Sichuan/成都 ChengduSichuan/自贡 Zigong(etc.)

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GDP & vehicles per capita — the data~320 prefectures 1998–2011 and provinces 1992–2011

0 1 2 3 4 5 6 7 8 9

0

0.1

0.2

0.3

GDP [104 CNY/person]

Vehic

leow

ner

ship

[Veh

icle

s /p

erso

n]

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Extensions & ongoing workRegional framework � incorporation of data & studies onheterogeneous transport system

Characteristics & trends of province-level transport:É Freight- or road-intensive; link to economic structure.É Freight network and content transformation � increasing

road shares.É Income elasticity of vehicle ownership.3

Transport-specific policies that differ by province:É Vehicle emissions & fuel quality standards � air pollution� health impacts. e.g. Beijing will reach China 5/Vstandard earlier than other regions.

É Fuel economy of vehicles.É Ownership & driving restrictions.É Infrastructure expansion.

Interaction with economic, energy & climate policy.3May be national, or global—but do delay, technology, policy effects modify?

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References I

Babiker, Mustafa, Angelo Gurgel, Sergey Paltsev, and John Reilly (2009). “Forward-looking versusrecursive-dynamic modeling in climate policy analysis: A comparison.” In: Economic Modelling 26.6,pp. 1341–1354. ISSN: 0264-9993. DOI: 10.1016/j.econmod.2009.06.009. URL:http://www.sciencedirect.com/science/article/pii/S0264999309001035.

Karplus, Valerie J., Paul Natsuo Kishimoto, and Sergey Paltsev (2012). “The Global Energy, CO2 Emissions, andEconomic Impact of Vehicle Fuel Economy Standards.” In: Environmental & Resource Economics (revised &resubmitted).

Karplus, Valerie J., Sergey Paltsev, Mustafa Babiker, and John M. Reilly (2013). “Applying engineering and fleetdetail to represent passenger vehicle transport in a computable general equilibrium model.” In: EconomicModelling 30, p. 295. DOI: 10.1016/j.econmod.2012.08.019.

Kishimoto, Paul Natsuo, Sergey Paltsev, and Valerie J. Karplus (2012). The Future Energy and GHG EmissionsImpact of Alternative Personal Transportation Pathways in China. Report No. 231. Available at:http://globalchange.mit.edu/research/publications/2327. Cambridge, MA, USA: MIT Joint Program onthe Science & Policy of Global Change.

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Paltsev, Sergey, John Reilly, Henry Jacoby, R. Eckaus, J. McFarland, M. Sarofim, M. Asadoorian, and M. Babiker(2005). The MIT Emissions Prediction and Policy Analysis (EPPA) Model: Version 4. Report 125. Available at:http://globalchange.mit.edu/research/publications/697. Cambridge, MA: MIT Joint Program on theScience and Policy of Global Change.

Paltsev, Sergey, Laurent Viguier, Mustafa Babiker, John Reilly, and K.H. Tay (2004). Disaggregating HouseholdTransport in the MIT-EPPA Model. Technical Note No.5.http://globalchange.mit.edu/research/publications/522. Cambridge, Massachusetts, USA: MIT JointProgram on the Science & Policy of Global Change.

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Schäfer, Andreas, John B. Heywood, Henry D. Jacoby, and Ian A. Waitz (2009). Transportation in aClimate-Constrained World. Cambridge, MA, USA: MIT Press.

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Zhang, Da, Sebastian Rausch, Valerie J. Karplus, and Xiliang Zhang (2013). “Quantifying regional economicimpacts of CO2 intensity targets in China.” In: Energy Economics 40, pp. 687–701. DOI:10.1016/j.eneco.2013.08.018.