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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
2
Outline
ReviewResearch questionsMethodology for regional & modal projection
Baseline projections
Policy analysis exampleFuel + sectoral output taxesBroader economic impactTransport sector impacts
Extensions
4
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.
5
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?
6
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.
7
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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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)
13
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
14
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
15
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
16
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
17
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
18
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
20
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
22
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
23
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
24
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
26
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.)
27
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?
29
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.
Kishimoto, Paul Natsuo, Da Zhang, Xiliang Zhang, and Valerie Jean Karplus (2013). “Modeling RegionalTransportation Demand in China and Impacts of a National Carbon Policy.” In: Transportation ResearchRecord (in press).
Narayanan G., Badri, Angel Aguiar, and Robert MacDougall (2012). Global Trade, Assistance, and Production: TheGTAP 8 Data Base. Tech. rep. Available online at: https://www.gtap.agecon.purdue.edu/databases/v8/.West Lafayette, Indiana, USA: Center for Global Trade Analysis, Purdue University.
National Bureau of Statistics of China (2008). China Energy Statistical Yearbook. Ed. by China Statistics Press.2008. Beijing, China.
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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References IIRausch, Sebastian, Gilbert E. Metcalf, and John M. Reilly (2011). “Distributional Impacts of carbon pricing: A
general equilibrium approach with micro-data for households.” In: Energy Economics 33.Supplement 1,S20–S33. DOI: 10.1016/j.eneco.2011.07.023.
Schäfer, Andreas, John B. Heywood, Henry D. Jacoby, and Ian A. Waitz (2009). Transportation in aClimate-Constrained World. Cambridge, MA, USA: MIT Press.
Schmalensee, Richard, Thomas M. Stoker, and Ruth A. Judson (1998). “World Carbon Dioxide Emissions:1950–2050.” In: Review of Economics and Statistics 80.1, pp. 15–27. ISSN: 1530-9142. DOI:10.1162/003465398557294.
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.
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