The Urban Future
Ed Glaeser
The Central Paradox
 Why is it that in an era in which
transportation and communication costs
have virtually vanished, cities have become
more important than ever?
 Urban resurgence is visible in high income
levels, robust housing prices, and a
concentration of innovation in urban areas.
 This is even clearer in the developing world.
Families: Median family income
San
Jose
Bethesda
80000
Bridgepo
Boulder,
Ann Arbo
60000
40000
.
Nassau-S
San Fran
Cambridg
Lake Cou
Washingt
Edison,
Warren-F
OaklandTrenton-
Newark-U
Minneapo
Oxnard-TSeattle-Hartford
Essex CoSanta An Boston-Q
Rockingh
RaleighAnchorag Santa
Manchest
Camden,
Ro
Santa Cr
Poughkee
Napa, CA
Madison,
Blooming
VallejoWilmingt
New
Have
Honolulu
HollandNorwichChicagoBaltimor
Austin-R
AtlantaFort Col Rocheste Monroe,
Worceste
Appleton
Iowa Cit
Dallas-P Milwauke Philadel
Racine,
CorvalliBurlingt LansingIndianap
Des
Moin
Kansas
C
Portland
Charlott
Albany-S
Charlott
Olympia,
Cincinna
Richmond
Salt Columbus
Lak
Naples-M Santa
Barnstab
Cedar
Ra
Green
Ba
Ogden-Cl
Allentow
Reno-Spa
Rocheste
Colorado
Omaha-Co
Lawrence
Ba
Sacramen
OshkoshSheboyga
Bremerto
Springfi
Durham,
St. Loui
West
Pal
Lincoln,
Kokomo,
Fort
Wor
San
Dieg
Janesvil
Rockford
Grand
Ra
Reading,
Providen
Harrisbu
Nashvill
Portland
Gary,
IN O Clevelan
Wausau, Champaig
Fort
Way
Huntsvil
Lancaste
San Luis
Akron,
York-Han
Peoria,
Tacoma,
Kalamazo
SiouxPhoenixFaLafayett
Atlantic
Lexingto
HoustonSt.
Clou
Wichita,
Dayton,
Ocean
CiSpringfi
Kennewic
Jackson,
Fargo,
NSalinas,
State
Co
Las
Vega
Fort
Topeka,
ElkhartWarner
R Syracuse
Columbia
La
Cross
Pittsfie
Toledo,
Flint,
MLau
WinstonBismarck
Jacksonv
Santa
Fe
Provo-Or
Davenpor
Louisvil VirginiaDetroitGreeley,
Bellingh Eau Clai
Boise
Ci
South Be BuffaloColumbia
Greensbo
Kankakee
Tallahas
Lebanon,
Dubuque,
Sarasota
Springfi
Evansvil
Orlando,
Bay
City
Greenvil
Waterloo
Fort
Wal
Roanoke,
Palm
Bay
Pittsbur
Decatur,
Savannah
Blooming
Midland,
Canton-M
Battle
CLittle
Salem,
O
Stockton
Charlest
Birmingh
Knoxvill
Wilmingt
Auburn-O
Hagersto
Duluth,FoRiversid
Memphis,
Anderson
Niles-Be
Cheyenne
RTulsa,
Los Ange
Spokane,
Dover,
D
SaginawBurlingt
Cape
Cor
Sioux
Ci
Port
St.
Flagstaf
Grand
O
Albuquer
Gainesvi
Tampa-St
Muskegon
Binghamt
Baton
Ro
Casper, Missoula
Athens-C
Vineland
Muncie,
Spartanb
Chattano
Eugene-S
Mansfiel
Montgome
ShermanJackson,
AugustaErie,
PA
Oklahoma
Billings
Lima,
OH
Modesto,
Macon,
G Anto
HickoryTyler,
TElmira,Scranton
San
Youngsto
College
Tucson,
Pascagou
Utica-Ro
Owensbor
Anderson
Amarillo
Lewiston
Jackson,
Decatur,
Lynchbur
Ashevill
Rapid
Ci JuMedford,
Pocatell
Lafayett
Victoria
Pensacol
Tuscaloo
Grand
Greenvil
Fayettev
Beaumont
Panama
C
Myrtle
B
New Orle
Punta
Go
St.
Jose
Terre
Ha
Bangor,
Lake
Cha
DeltonaSpringfi
Lakeland
Waco,
TX
Rocky
Mo
Wichita
Charlest
Longview
Williams
Gulfport
Chico,
C
Fayettev
KilleenColumbus
Lubbock,
Florence
Parkersb
Corpus
C
Goldsbor
Redding,
Mobile,
Clarksvi
Shrevepo
Houma-Ba
Pueblo,
Altoona,
Florence
Yuba
CitCumberla
Great
Fa
Abilene,
Anniston
Yakima,
Dothan,
Texarkan
Monroe,
San
Ange
Bakersfi
Albany,
Kingspor
Madera,
Lawton,
Wheeling
Danville
Sumter,
Joplin,
Gadsden,
Jonesbor
Hattiesb
Fresno,
Johnson
Merced,
Pine
Blu
Ocala,
FSmi
Fort
Huntingt
Jacksonv
Odessa,
Visalia-Alexandr
Yuma, AZ
Las Cruc
El Paso,
Laredo,
New York
Brownsvi
McAllen-
20000
4
6
8
Density
10
12
Urbanization Across the World
% population urban 1998, WDI200
Fitted values
HongSingapor
Kon
Belgium
100
Iceland
Bahrain Israel Malta
Luxembou
United
K
Argentin
Netherla
Bahamas,
Germany,
Venezuel
New
Zeal
Chile
United
ADenmark
Australi
Saudi Ar
Sweden
Korea
Brazil
Gabon
Japan
Spain
Canada
United S
France
Norway
Mexico
Trinidad
Jordan Peru Colombia
Turkey
ItalySwitzerl
Finland
Poland Hungary
Austria
Tunisia
Dominica
Ecuador
Bolivia
Portugal
Congo
Iran, I.
Greece
Algeria
Ireland
Philippi Panama
Malaysia
Cyprus
Jamaica
MauritanNicaragu
Morocco
Paraguay
Syria
South af
Honduras
Botswana
Fiji
Costa Ri
Cameroon
Senegal
Cote d'I
Egypt El Salva
Nigeria
Mauritiu
Benin Central
Zambia
Guatemal
Indonesi
Mozambiq
Ghana
Guyana
Sierra L
HaitiPakistan Zimbabwe
Sudan
Angola
Togo
Kenya Comoros
Guinea
Gambia
China
Tanzania
Zaire
Mali
Madagasc
India
Lesotho
Swazilan
Yemen,Chad
N
Banglade
Sri Lank
Guinea-B
Malawi
W estern Thailand
Niger
Papua Ne
Ethiopia
Uganda
Nepal
Burundi
Uruguay
50
0
6
8
Log of GDP 1998
10
The Hypothesis
 One major effect of globalization has been an increase in being
smart.
 You become smart by being around other smart people– we are a
social species.
 Cities, like Boston and New York and London and Mumbai make
that possible.
 The same death of distance that did so much to hurt Detroit
helped save Boston and NYC.
The Gifts of Urban Density
 Art in Flanders (van Eycks, Campin, Memling)
 Commercial patrons and learning
 Religion
 The Brethren of the Common Life (Adrian IV, Erasmus, Martin
Luther)
 Education and Literacy
 Caxton and Gutenberg
 Political unrest and democracy (Coninck)
Chicago History
 America has an enormously fertile hinterland, but it is
extremely expensive to access.
 In 1816, cost of moving goods 32 miles is more expensive
than moving across the Atlantic.
 The big story of the 19th century is the construction of a
transport network– water and rail that allows access to the
hinterland.
Chicago Continued
 Chicago is the creation of two canals: Illinois and Michigan
and Erie, and then rails.
 First exporting live beef and salted pig.
 Pigs are corn with feet, and Chicago is allowing access to
high productivity Iowa land (about 50 percent more fertile
than Kentucky).
 Then as transport costs fall (and refrigeration) there is a
move to dressed beef and wheat.
Buenos Aires
 Buenos Aires is also a port that moves agricultural products
to eastern markets.
 Starts with lower value, but highly durable goods (hide and
tallow trade).
 Steam and frigorificos make it possible to shift chilled beef
and mutton.
 As rail got more effective, grain came to supplement the
animal stocks.
Four Differences
 Wages
 Schooling
 Industrialization
 Politics
Income Levels
 Buenos Aires was rich in 1900, but it was still much less rich
than Chicago.
 Begs two questions. Labor Demand– why were workers
more productive in Chicago?
 Physical capital, human capital technology
 Labor Supply– why did workers go to Buenos Aires if real
wages were lower?
 Culture, climate (Italians and Spanish)
Literacy in the Two Cities
• Enrollment data suggest that, after 1884, the issue isn’t
education in Buenos Aires.
• Part of this is the nature of immigrants.
– In 1914, 88 percent of Chicago immigrants could read, but only
72 percent of BA immigrants.
• Part of this is still native born.
– In 1895, literacy rate of native born in BA is 75 percent; in
Chicago it is percent.
– Common schools in US vs. ? In Argentina.
Industry in the Two Cities
 In 1914, Capital per worked is 2.25 times higher in Chicago
than in BA.
 Value added per worker is 2.44 times higher in Chicago than
in BA.
 Roughly commensurate with the wage differences that we
see.
 Capital alone does not explain the wage differences.
Why Does Chicago Stay Rich and
Buenos Aires Decline?
 Levels of Physical Capital
 In 1914, Capital per worked is 2.25 times higher in Chicago
than in BA; value added is 2.5 times higher
 Levels of Human Capital
 In 1895, literacy rate of native born in BA is 75 percent; in
Chicago it is 98 percent.
 Technology
 Chicago is on the cutting edge (cars, skyscrapers)
 Political Institutions
The Problematic 20th Century
 The Automobile made public transportation oriented
cities seem somewhat obsolete.
 The truck freed manufacturing from needing to cluster
around ports and rail stations.
 Declining transport costs created a rise in consumer
cities over cities oriented around productive advantages
like waterways.
The Decline of the Costs of Moving Goods
Dollars per Ton Mile (Real)
.185063
.02323
1890
2000
year
Railroad Revenue per Ton Mile
The Move to Warmth
Fitted values
Log Change in Population 1980-2
Las Vega
1
Austin-S
Phoenix- Orlando,
West Pal
Atlanta,
RaleighDallas-F
Sarasota
Bakersfi
Sacramen
Stockton
Fresno, Tucson,
Charlott
Jacksonv
SanTampa-St
Dieg
HoustonDenver-B
SeattleMiami-Fo
Salt
Lak
San Anto
Nashvill
Portland
Los Ange
El Paso,
Albuquer
Minneapo
Greensbo
Augusta- San Fran
Washingt
Columbia
Richmond
NorfolkGrand RaLancaste
Greenvil
Lexingto
Charlest
Columbus
Oklahoma
Knoxvill
Des MoinKansas
Wichita,
Little
RJackson,Baton
Indianap
C
Spokane,
Tulsa,Memphis,
O
Ro
Mobile,
Omaha, N
Corpus C
Lafayett
Allentow
Honolulu
Cincinna
Rockford
Boston-W
Harrisbu
Birmingh
Fort Way
ChicagoNewJohnson
YorkChattano
Providen
Philadel
Hartford
New
Have
St.
Loui
Milwauke
Kalamazo
Louisvil
LansingRocheste
Albany-S
Springfi
Shrevepo
DetroitBeaumont
New Orle
Syracuse
Dayton-S
Canton-M
Clevelan
Toledo,
SaginawPeoria-P
Scranton Huntingt
BuffaloDavenpor
Youngsto
Pittsbur
.5
0
0
20
40
January mean temperature 1980
60
80
Figure 24: 1980-2000 Population Growth and Mean January Tmp.
Population Grow th 20-80
2.46159 SAN DIEG
Fitted values
HOUSTON,
JACKSONV
EL DALLAS,
PASO,
LOS ANGE
TULSA, O
SAN ANTO
OKLAHOMA
MEMPHIS,
WICHITA,
NASHVILL
FORT WOR
COLUMBUS
NORFOLK,
INDIANAP
ATLANTA,
FORT WAY
YONKERS,
DENVER,
FLINT, M
SAVANNAH
OMAHA,
N
SPOKANE,
PEORIA,
TACOMA,
KANSAS
C
BIRMINGH
OAKLAND,
SEATTLE,
SOUTH
BE
EVANSVIL
DESORLE
MOIN
TOLEDO,
WASHINGT
NEW
PORTLAND
MILWAUKE
KANSAS ALLENTOW
C GRAND RA
SALT
LAK
SAN FRAN
ERIE, PA RICHMOND DAYTON,
LOUISVIL
NEW YORK
DETROIT,
SPRINGFI
MANCHEST
SIOUX
CI
ST.
PAUL
O
WATERBUR
CHICAGO,
ELIZABET
CANTON, AKRON,
BALTIMOR
PATERSON
BRIDGEPO
SYRACUSE
HARTFORD
ST. JOSE
MINNEAPO
CINCINNA
DULUTH,
PHILADEL
WORCESTE
ALBANY,
YOUNGSTO
CAMBRIDG
BAYONNE,
ROCHESTE
NEW
BEDF
UTICA,
N NYLOWELL,
NEWARK,
TROY,
NEW HAVESCHENECT
TRENTON,
FALL RIV
CAMDEN, BOSTON,
READING,
PITTSBUR
CLEVELAN
BUFFALO,
HARRISBU
WILKES-B
LAWRENCE
PROVIDEN
SCRANTON
WILMINGT
ST. LOUI
KNOXVILL
-.545373
SOMERVIL
JERSEY C
23869.5
947.754
dens20
Figure 8: Density and City Growth 1920-1980
The Rebirth of Boston, NYC
 Idea-oriented industries rose in places that were once
centers of manufacturing.
 New York’s early industries are sugar refining, publishing
(stolen books) and garments
 Finance in New York and an urban chain of ideas
 Understanding risk and return with data
 The sale of riskier assets (Milken)
 The use of risky assets to restructure companies (KKR)
 The nationwide sharing of risk (Ranieri and MBSs)
 The sale of data tools (Bloomberg)
Figure 4:
Population Growth for MSAs in the Northeast and Midwest
60%
Average Population Growth (1970-2000
53%
50%
40%
30%
20%
20%
10%
15%
15%
7.5% - 9.5%
9.5% - 11%
8%
0%
0 - 7.5%
Data from the United States Census
Percent of Population with a BA (1970)
11% - 15%
15%-31%
Fitted values
Change Income 1980-2000
.6
San Fran
Austin-R
Boston-Q
San Jose
Portland
Newark-U
Providen
AtlantaTampa-St
CharlottAlbany-S
Jacksonv
Philadel
West Pal
Nashvill
Columbus
Hartford
NewNassau-S
York Minneapo
Springfi Knoxvill
Baltimor
Memphis,
San
Dieg
Orlando,
Sacramen
Oxnard-T
Richmond
Greensbo
BirminghOmaha-Co
Columbia
Cincinna
Fort Jackson,
Lau
Charlest
Tacoma,
Dallas-P
Little
R
Kansas C
LouisvilSaltIndianap
LakHarrisbu
San AntoAllentow
Ra St. Loui
Lancaste Grand
ChicagoStockton
Spokane,
PhoenixSyracuse
Honolulu
BuffaloWichita, Albuquer
Akron,Dayton,
O
Riversid
Tucson,
Milwauke
Las Vega
Rocheste
Toledo,
Fort Way
Clevelan
Tulsa, O
Pittsbur
Baton Ro Oklahoma
Los Ange
New
Orle
DetroitCanton-M
.4
.2
Youngsto
Fresno,
Gary, IN
Bakersfi
El Paso,
Washingt
Seattle-
Houston-
0
.02
.04
.06
1980 Share of Skilled Workers
Figure 5
.08
.1
Fitted values
Log Wage Residual 2000
New York
10.2
Minneapo
Charlott
AtlantaWest Pal
10
9.8
Kansas C
Indianap
Richmond
Louisvil
PhoenixOmaha-Co
San Dieg Columbus
Albany-S
Las Vega
Nashvill
St. Loui
Grand
Wichita,
Fort Ra
Way
Honolulu
Salt Lak
Columbia
Lancaste
Greensbo Orlando,
Dayton,
Harrisbu
Toledo, Allentow
Tulsa, O
Albuquer
Tampa-St
Syracuse
Charlest
ChicagoBirmingh
Rocheste
Canton-M
Little
R
Pittsbur
Spokane,
BuffaloBaton
Ro
San
Anto
Stockton
Jackson,
Knoxvill
Youngsto
Tucson,
Oklahoma
Memphis,
Austin-R
San Fran
New Orle
Bakersfi
Fresno,
9.6
.05
.1
2000 Share of Skilled Workers
Figure 4
.15
.1
Change in Percent with BA 1990-2000
0
.05
.1
.2
.3
.4
Percent of Adults with BA Degree in 1990
.5
Figure 6:
Productivity in More Skilled MSAs
90,000
80,583
80,000
74,759
70,000
Output per Worker
64,005
60,000
59,016
55,975
50,000
40,000
30,000
20,000
10,000
0
Less than 125,000
125,000 to 300,000
300,000 to 750,000
750,000 to 1,000,000
Above 1,000,000
Population
Units of observation are MSAs where the share of adults with college degrees is greater than or equal to 17.65%. Labor force and population is from the
Census. Gross Metropolitan Product is from the Bureau of Economic Analysis.
Other Determinants of
Economic Success
 Strong correlation between growth and an abundance of
small firms.
 Detroit goes from being a highly productive city of small
entrepreneurs to being stagnant.
 A weaker correlation between industrial diversity and later
growth.
 Few strong correlations with particular industries (except for
healthcare which is -);,m
-1
Employment Growth, 1977-2000
0
1
2
3
4
Figure 7:
Employment Growth and Average Firm Size, by MSA
5
10
15
Workers per Firm, 1977
20
25
Reinvention in the West
 In the old world, Milan thrives and Turin fades.
 Weaving becomes fashion.
 Chains of ideas (Nino Cerruti, Armani, others).
 Minneapolis excels and Cleveland doesn’t.
 Birmingham reinvents itself (it always was an intellectual polis);
Manchester doesn’t.
 And of course, then there is Boston.
 Technology, medicine, finance.
Urban Intercontinental Gateways
 Athens as importer of ideas from the Greek Diaspora.
 Anaxogoras, Prodicus came to Athens-> Socrates
 Baghdad is importer of ideas from India, Persia, and Greece.
 Jafar is a Barmakid– probably a Brahmin from Kashmir– aided the
Abbasid takeover
 Academy of Gundishapur, Sindhin, Al-Khwarizmi
 Cordoba, Venice and the transmission west.
Bangalore and Others
 The Most Successful Cities today continue to be gateways





across civilizations– conduits for the flow of ideas.
Bangalore in India and software services
New York-London-Singapore financial nexuses.
Hong Kong and the connection to China
Singapore and the Pacific.
Flat world has increased their importance.
What is good about urban poverty?
 Cities tend to contain a large number of poor people,
but that reflects urban strengths more than urban
weaknesses.
 In cities, there is opportunity, ethnic networks, and life
without cars.
 Cities aren’t making people poor, they are bringing
them in.
 Policies that are good to poor people in cities will attract
more of them and that is o.k.– the really problem is the
artificial equality of suburbs.
The Rise of the Consumer City
 While clusters of genius are more important than ever, they are
no longer tied down by productive amenities like rivers and
ports
 Increasingly, cities have formed in places where people want to
live.
 At the same time, more attractive older cities have become
increasingly attractive to people who want to live in a dense
environment.
When are high real wages bad?
Declining Real Wages and the Rise of the
Consumer City
Why are so many people
still in the rustbelt?
 The rustbelt was built on manufacturing around the
waterways.
 Erstwhile creative hubs like Detroit evolved into goods
producing machines, but declining transport costs led
manufacturing to move.
 Now there is little obvious comparative advantage to these
places and the weather isn’t great.
Are some cities becoming gateless gated
communities?
 Many cities have place extreme limits on the ability to build up




(Mumbai’s historical FAR was 1.25).
In older places, historic preservation joins height limitations.
In suburbs, the limits are minimum lot sizes.
These limits are supply restrictions that brake the growth of
attractive areas.
This is where Jane Jacobs was wrong.
Prices and Permits across
Larger Metropolitan Areas
San Fran
600000
San Jose
New York
2005 Housing Price
Honolulu
Santa An
OaklandOxnard-T
Bridgepo
400000
San Dieg
Los Ange
Bethesda
Nassau-S
Cambridg
Washingt
Newark-U
Essex
Co
Boston-Q
Edison Seattle-
Sacramen
Riversid
West Pal
Poughkee
Fort
Lau
Providen
New
Have Worceste
Lake Cou
Portland
Sarasota
Chicago- Fresno
Hartford
Baltimor
Tacoma
Minneapo
Wilmingt
PhoenixCamden
Portland
Springfi
Salt Bakersfi
Lak Madison PalmColorado
Warren-F
Virginia
Bay
Philadel
OrlandoTucson
AtlantaAlbany-S
MilwaukeAllentow
RaleighCharlest
Jacksonv
Tampa-St
DeltonaAlbuquer
Richmond
Austin-R
Nashvill
Charlott
Columbus
Clevelan
Harrisbu
Cincinna
Grand
Ra Kansas C Indianap
New
Orle
Akron
Lakeland
St.
Loui
Gary
Dallas-P
Louisvil
Knoxvill
Greensbo
Greenvil
Birmingh
Columbia
Omaha-Co
DetroitToledo
Dayton
Baton
Chattano
Scranton
Rocheste
BuffaloSyracuse
Pittsbur
AugustaLittle Ro
RMemphis
FortHoustonWor
Oklahoma
San Anto
Tulsa
Youngsto
Wichita
El Paso
200000
Las Vega
Cape Cor
0
0
.1
.2
Permits 2000-5/Stock in 2000
.3
Difference in Index May 2006 to May 2008
-80
-60
-40
-20
0
Charlotte-Gastonia-Rock Hill, NC-SC
Seattle-Bellevue-Everett, WA
Portland-Vancouver, OR-WA
Dallas, TX
Atlanta,
GA
Denver,
CO
Cleveland-Lorain-Elyria, OH
Chicago,
IL
Boston, MA-NH
New York, NY
Detroit, MI
Minneapolis-St. Paul, MN-WI
Washington, DC-MD-VA-WV
San Francisco, CA
Tampa-St. Petersburg-Clearwater, FL
Phoenix-Mesa, AZ
San Diego, CA
Las Vegas, NV-AZ
Los Angeles-Long Beach, CA
Miami, FL
0
50
100
150
Difference in Index May 2001 to May 2006
200
Green Cities
 Urban residents are much less likely to drive than their suburban
counterparts.
 Urban residents live in smaller homes that use less energy.
 Since we don’t tax carbon properly, this means that there are too
few people in cities.
 The environmental consequences of environmentalism.
Wharton Regulation Index
10
Los Ange
San Fran
5
.
Seattle-
San
Jose
San
Dieg
Salt Lak
Fort Lau
Sacramen
New York Boston,
Portland
Syracuse
CincinnaTulsa, O
Charlott
Birmingh
Nashvill
Miami-Hi
Philadel
Pittsbur Akron, O
Houston-Memphis,
Milwauke
Baltimor
Minneapo
Providen
Orlando, New Orle
Hartford Columbus
Tampa-St
Washingt
Albany-S
Atlanta,
Greensbo Detroit,
Rocheste
Phoenix,
Grand Ra
Louisvil
Indianap
Kansas C
Oklahoma
Clevelan
Denver-B
San
St.Anto
Loui
ChicagoDayton-S
Dallas-F
0
-5
Buffalo-
Richmond
-10
800
1000
1200
Total Cost from Marginal Home
1400
1600
City-Suburb Differentials
 For each metropolitan area, we can also calculate the difference
between urban and suburban energy usage.
 Calculate gas usage by central city vs. suburb.
 Convert public transit by ridership using census figures.
 Calculate energy spending using the IPUMS for central city vs.
suburb.
Rank
City
Electricity
Coal
LPG
Coal
gas
Car
Taxi
Bus
1
Huaian
0.879
0.098
0.082
0.016
0.120
0.011
0.023
1.230
0.090
2
Suqian
0.865
0.218
0.117
0.000
0.006
0.026
1.231
0.073
3
Haikou
0.983
0.007
0.176
0.015
0.000
0.006
0.065
1.252
0.124
4
Nantong
1.062
0.036
0.164
0.000
0.007
0.012
1.281
0.080
5
Nanchang
0.978
0.141
0.048
0.000
0.007
0.130
1.305
0.138
70
Yinchuan
0.675
0.059
0.036
0.338
0.034
0.095
2.287
3.543
0.146
71
Qiqihaer
0.765
0.054
0.115
0.000
0.018
0.041
2.620
3.614
0.085
72
Beijing
1.558
0.145
0.049
0.084
0.650
0.018
0.138
1.306
3.997
0.192
73
Mudanj’ng
1.047
0.136
0.081
0.353
0.040
0.017
3.154
4.827
0.107
74
Daqing
0.998
3.719
5.115
0.056
Mean
1.122
1.228
2.177
0.134
0.019
0.093
0.233
0.003
0.000
0.026
0.137
0.102
0.077
0.135
0.016
0.067
Rail
0.049
0.036
Heating
Total
CO2
Standard
error