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Energy and material flows of megacities
Christopher A. Kennedy
a,1
, Iain Stewart
a
, Angelo Facchini
b
, Igor Cersosimo
b
, Renata Mele
b
, Bin Chen
c
, Mariko Uda
a
,
Arun Kansal
d
, Anthony Chiu
e
, Kwi-gon Kim
f
, Carolina Dubeux
g
, Emilio Lebre La Rovere
g
, Bruno Cunha
g
,
Stephanie Pincetl
h
, James Keirstead
i
, Sabine Barles
j
, Semerdanta Pusaka
k
, Juniati Gunawan
k
, Michael Adegbile
l
,
Mehrdad Nazariha
m
, Shamsul Hoque
n
, Peter J. Marcotullio
o
, Florencia González Otharán
p
, Tarek Genena
q
,
Nadine Ibrahim
a
, Rizwan Farooqui
r
, Gemma Cervantes
s
, and Ahmet Duran Sahin
t
a
Department of Civil Engineering, University of Toronto, Toronto, ON M4J 3K1, Canada;
b
Enel Foundation, 00198, Rome, Italy;
c
School of Environment,
Beijing Normal University, Beijing, China 100875;
d
Department of Energy and Environment, TERI University, Vasant Kunj, New Delhi, DL 110070, India;
e
Department of Industrial Engineering, De La Salle University, Malate, Manila, 1004 Metro Manila, Philippines;
f
Department of Landscape and Ecological
Planning, Seoul National University, Seoul, South Korea 151-742;
g
Coimbra Institute of Postgraduate Research in Engineering, Federal University of Rio de
Janeiro, University City, Rio de Janeiro, RJ 21941-901, Brazil;
h
Institute of the Environment and Sustainability, University of California, Los Angeles, CA
90095;
i
Department of Civil and Environmental Engineering, Laing O’Rourke Centre for Systems Engineering and Innovation, Imperial College London,
London SW7 2AZ, United Kingdom;
j
Institute of Geography, University of Paris, 75005 Paris, France;
k
Department of Accounting, Trisakti University, Jakarta
Barat, DKI Jakarta 11440, Indonesia;
l
Department of Architecture, University of Lagos, Lagos 23401, Nigeria;
m
Department of Environmental Engineering,
College of Engineering, University of Tehran, Tehran, Iran;
n
Department of Civil Engineering, Bangladesh University of Engineering and Technology, Dhaka-
1000, Bangladesh;
o
Department of Geography, Hunter College, New York, NY 10065;
p
Environmental Strategies Department, Environmental Protection
Agency, Government of Buenos Aires City, Buenos Aires, Argentina;
q
EcoConServ Environmental Solutions, Zamalek, Cairo, Egypt 11211;
r
Department of
Civil Engineering, Faculty of Civil Engineering and Architecture, NED University of Engineering and Technology, Karachi 75270, Pakistan;
s
Department of
Civil Engineering, University of Guanajuato, CP 36000, Guanajuato, Mexico; and
t
Faculty of Aeronautics and Astronautics, Istanbul Technical University,
Maslak, 34469, Istanbul, Turkey
Edited by Susan Hanson, Clark University, Worcester, MA, and approved April 2, 2015 (received for review March 6, 2015)
Understanding the drivers of energy and material flows of cities is
important for addressing global environmental challenges. Accessing,
sharing, and managing energy and material resources is particularly
critical for megacities, which face enormous social stresses because of
their sheer size and complexity. Here we quantify the energy and
material flows through the world’s 27 megacities with populations
greater than 10 million people as of 2010. Collectively the resource
flows through megacities are largely consistent with scaling laws
established in the emerging science of cities. Correlations are estab-
lished for electricity consumption, heating and industrial fuel use,
ground transportation energy use, water consumption, waste gener-
ation, and steel production in terms of heating-degree-days, urban
form, economic activity, and population growth. The results help
identify megacities exhibiting high and low levels of consumption
and those making efficient use of resources. The correlation between
per capita electricity use and urbanized area per capita is shown to be
a consequence of gross building floor area per capita, which is found
to increase for lower-density cities. Many of the megacities are
growing rapidly in population but are growing even faster in terms
of gross domestic product (GDP) and energy use. In the decade from
2001–2011, electricity use and ground transportation fuel use in
megacities grew at approximately half the rate of GDP growth.
sustainability
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sustainable development
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urbanization
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urban metabolism
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industrial ecology
The remarkable growth of cities on our planet during the past
century has provoked a range of scientific inquires. From
1900–2011, the world’s urban population grew from 220 million
(13% of the world’s population) to 3,530 million (52% of the
world’s population) (1, 2). This phenomenon of urbanization has
prompted the development of a science of cities (3, 4), including
interdisciplinary contributions on scaling laws (5, 6), networks (7),
and the thermodynamics of cities (8, 9). The growth of cities also
has been strongly linked to global challenges of environmental
sustainability, making the study of urban energy and material
flows, e.g., for determining greenhouse gas emissions from cities
and urban resource efficiency (10–19), important.
At the pinnacle of the growth of cities is the formation of
megacities, i.e., metropolitan regions with populations in excess of
10 million people. In 1970, there were only eight megacities on the
planet (SI Appendix,Fig.S1). By 2010, the number had grown to 27,
and a further 10 megacities likely will exist by 2020 (20). In 2010,
460 million people (6.7% of the global population) lived in the 27
megacities. The sheer size and complexity of megacities gives rise to
enormous social and environmental challenges. Megacities often
are perceived to be areas of high global risk (i.e., threatened by
economic, environmental, geopolitical, societal, and technological
risks with potential impacts across entire countries) with extreme
levels of poverty, vulnerability, and social–spatial fragmentation
(21–24). To provide adequate water and wastewater services,
many megacities require massive technical investment and ap-
propriate institutional development (25, 26). Many inhabitants of
megacities also suffer severe health impacts from air pollution
(27). However, these factors present only one side; the megacities
include some of the wealthiest cities in the world (albeit with large
disparities between citizens). Even the poorer megacities are seen
by some as potential centers of innovation, where high levels of
resource efficiency might reduce global environmental burdens
(21, 28, 29). Whether megacities can develop as sustainable cities
depends to a large extent on how they obtain, share, and manage
their energy and material resources.
Significance
Our quantification of energy and material flows for the world’s
27 megacities is a major undertaking, not previously achieved.
The sheer magnitude of these flows (e.g., 9% of global electricity,
10% of gasoline; 13% of solid waste) shows the importance of
megacities in addressing global environmental challenges. In
aggregate the resource flows through megacities are consistent
with scaling laws for cities. Statistical relations are established for
electricity use, heating/industrial fuels, ground transportation,
water consumption, waste generation, and steel production in
terms of heating-degree days, urban form, economic activity, and
population growth. Analysis at the microscale shows that elec-
tricity use is strongly correlated with building floor area,
explaining the macroscale correlation between per capita
electricity use and urbanized area per capita.
Author contributions: C.A.K., A.F., I.C., R.M., B. Chen, A.K., A.C., K.-g.K., C.D., E.L.L.R., S. Pincetl,
J.K., S.B., S. Pusaka, J.G., M.A., M.N., S.H., P.J.M., F.G.O., T.G., R.F., G.C., and A.D.S. designed
research; C.A.K., I.S., I.C., B. Chen, M.U., A.K., A.C., K.-g.K., C.D., E.L.L.R., B. Cunha, S. Pincetl,
J.K., S.B., S. Pusaka, J.G., M.A., M.N., S.H., P.J.M., F.G.O., T.G., N.I., R.F., G.C., and A.D.S. per-
formed research; C.A.K., I.S., A.F., I.C., M.U., J.K., and F.G.O. analyzed data; and C.A.K. and I.S.
wrote the paper.
The authors declare no conflict of interest.
This article is a PNAS Direct Submission.
1
To whom correspondence should be addressed. Email: christopher.kennedy@utoronto.ca.
This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.
1073/pnas.1504315112/-/DCSupplemental.
www.pnas.org/cgi/doi/10.1073/pnas.1504315112 PNAS Early Edition
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SUSTAINABILITY
SCIENCE
The aims of our study are first to quantify the energy and
material flows for the world’s 27 megacities, based on 2010
population, and second to identify physical and economic char-
acteristics that underlie these resource flows at multiple scales.
This goal entailed developing a commondata-collectionprocess
applied to all the megacities. The cities were identified based on
Brinkhoff’s database of metropolitan regions (www.citypopulation.
de/world/Agglomerations.html;SI Appendix,Fig.S2). The mega-
cities are essentially common commuter-sheds of more than 10
million people; most are contiguous urban regions, but a con-
tiguous area is not a requirement; for example, the London
megacity includes a ring of commuter towns outside the Greater
London area. Megacities can spread across political borders.
They include large tracts of suburban regions, which can have
higher per capita resource flows than central areas (30, 31). We
quantify energy flows for the dominant direct forms of con-
sumption in megacities. A wide and complex range of materials
flow through cities; here the focus is on water, concrete, steel, and
waste. We show how values of aggregate resource use of all
megacities generally are consistent with the scaling laws that have
been developed for cities (5, 6). We then analyze factors corre-
lated with energy and material flow at macro- and microscales;
discuss megacities with low, high, and efficient use of resources;
and examine changes over time.
Results
Total Resource Flows. Annual energy consumption in megacities,
for 2011, ranges from ∼78 PJ for Kolkata (population 16.3 mil-
lion) to ∼2,824 PJ for the New York Metropolitan Area (pop-
ulation 22.2 million) (Fig. 1A). Although Tokyo is the largest
megacity, with 34.0 million people, its energy consumption is
surpassed by New York because of New York’s higher con-
sumption of both transportation fuels (47 GJ per capita vs. 18 GJ
per capita in Tokyo; SI Appendix,Fig.S3) and heating/industrial
fuels(56GJpercapitavs.29GJpercapitainTokyo;SI Appendix,
Fig. S4); per capita electricity use is approximately equal in the two
megacities (SI Appendix,Fig.S5). Nine other megacities—Moscow,
Seoul, Los Angeles, Shanghai, Guangzhou, Osaka, Tehran,
Mexico City, and London—consume in excess of 1,000 PJ/y.
To put these figures in perspective, an oil supertanker can hold
about 12.2 PJ of oil (32); New York consumes the energy
equivalent of one supertanker approximately every 1.5 days.
Total water consumption is notably higher in New York (10.9
million ML), Guangzhou (9.80 million ML), Shanghai (9.75 mil-
lion ML), and Los Angeles (6.62 million ML) than in the other
megacities (Fig. 1B). In New York about 54% of the water is used
in thermoelectric plants. Water consumption in the remaining
megacities ranges from a low of 0.48 million ML in Jakarta to a
high of 4.19 million ML in Tokyo.
New York also exceeds other megacities in solid waste pro-
duction, both in absolute and per capita terms (Fig. 1Cand SI
Appendix, Fig. S6). One of the challenges with solid waste data
that we have observed in the past (13) is that the construction
sector often produces large quantities of waste (not always
counted in inventories), and commercial waste production can
be difficult to estimate when handled by the private sector.
Aggregate Resource Use and Scaling Laws. Although there is great
diversity in the energy and material flows through individual
megacities, collectively their resource flows are, with the exception
of gasoline, consistent with scaling laws observed for cities over a
wide range of populations (6). This consistency can be seen by
comparing the total resource flows of the megacities as a per-
centage of the world’s total with the percentage of global pop-
ulation living in megacities (Methods). Clearly megacities are at
the top of the population scale and should exhibit extreme values
for quantities that scale superlinearly or sublinearly. The 27
megacities had a combined population of 460 million in 2010,
equal to 6.7% of global population (Fig. 2). Their combined gross
domestic product (GDP) was much larger in percentage terms, at
14.6% of global GDP. This result is expected for socioeconomic
characteristics, which have been shown to scale superlinearly (6).
The total waste production for the megacities is estimated to
be 12.6% of the global amount. This value suggests that waste
production also may exhibit superlinear behavior, likely because of
its relation with GDP. Essentially the higher amount of economic
activity in larger cities entails importing relatively high quantities of
goods and other materials that, apart from those that become
bound in the building stock, leave cities relatively rapidly as wastes.
The total energy consumption of the 27 megacities is 26,347
PJ, which is ∼6.7% of global energy consumption. This percentage
is about the same as the percentage of global population that lives
in megacities. Bettencourt and colleagues (5, 6) found a mixture of
energy-related scaling relationships: Residential electricity scales
linearly, total electricity scales superlinearly, and gasoline use scales
sublinearly. We found megacities consumed 9.3% of global elec-
tricity and 9.9% of global gasoline; the former is consistent with
superlinear scaling, but the latter is not consistent with sublinear
scaling and requires further exploration (This sublinear scaling
could reflect the use of other transportation fuels in cities, e.g., the
high use of diesel in many European cities).
The observation that megacities consume 6.7% of total global
energy use also should be treated cautiously for the following rea-
sons. (i) The global energy use total includes energy consumed in
global aviation and marine transportation of goods and people;
Fig. 1. Resource flows for megacities in 2011. (A) Energy use. (B) Water use including line losses. (C) Municipal solid waste production. Values shown are for
the megacity populations scaled on a per capita basis from recorded data for the study area population (Methods).
Fig. 2. Megacity resource and waste flows as a percentage of world values.
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www.pnas.org/cgi/doi/10.1073/pnas.1504315112 Kennedy et al.
much of this transportation is between cities but is not reflected in
their recorded energy use here. (ii) We have reported final energy
consumption by cities, not primary energy use. Electrical energy use
would be higher if expressed in terms of primary energy input.
(iii) The extraction and refining of fossil fuels requires an energy
premium that necessarily occurs to combust fuels in cities. (iv)The
majority of megacities are in warm to hot climates where re-
quirements for heating are relatively low [only Moscow, Beijing,
Seoul, London, New York, Istanbul, and Paris-Isle-de-France recor-
ded more than 2,000 heating-degree-days (HDD) in 2011]. Whether
the distribution of climatic zones for megacities is representative of
that for all global inhabitants has not been established.
The final quantity compared in Fig. 2 is water use. The 78
million ML consumed in megacities (including losses) is about
3.0% of global water use, which is estimated to be roughly 2,600
million ML (33). This percentage seems reasonably consistent with
expectations, because a large amount of the global water supply is
used in agriculture, which is a predominantly rural activity (34).
Macroscale Correlations. Some understanding of the factors that
underlie the energy and material flows through megacities can be
established by first analyzing per capita rates at the macroscale.
There already is a large literature debating the relation between
urban transportation energy use and urban form (35). Essentially,
the literature shows that density (or, alternatively, urbanized area
per capita) displays a significant relation with urban transportation
energy if the dataset analyzed includes a wide spectrum of global
cities with a wide range of densities. When studies include only
cities within the same country or the same continent, for which
differences in density are less wide ranging, or examine microscale
features within cities, then density is found to be less significant
than other variables such as supply of public transit, spikiness, and
other characteristics of urban design (e.g., ref. 36). Previous re-
search also has found that per capita use of heating and industrial
fuels is significantly correlated with HDD (17). This known re-
lationship, as well as that for transportation energy use, also is
found to hold for the megacities, thereby corroborating the
dataset (Table 1). For the megacities, however, we also find a
significant correlation between heating and industrial fuel use per
capita and urbanized area per person.
Little previous research has explored differences in electricity use
between global cities. In our stepwise regression analysis we found
per capita electricity use in megacities to be significantly correlated
with urbanized area per capita (Table 1 and SI Appendix,Fig.S5).
Electricity use is known to be a strong determinant of economic
growth (37), and we also observe significant correlation between
per capita GDP and electricity use in the megacities (SI Appendix,
Fig. S7). Because there is relatively strong correlation between
urbanized area per capita and GDP per capita (SI Appendix,Cor-
rection for Multiple Inferences), the latter drops out of the stepwise
regression analysis, because it has less explanatory power than area
per person. We suspect that lower-density megacities such as Los
Angeles and New York have greater building floor space per capita,
leading to higher electricity consumption for lighting and other
building applications. We explore this possibility further in the
microscale correlation analysis that follows.
The macroscale analysis also revealed a correlation between
water consumption per capita and area per capita. Again, a weak
correlation was found with GDP if area per capita was omitted
from the model, but no relationships with precipitation or
cooling-degree-days (CDDs) were found. A different study for
Chinese and American cities found that urban water use per
capita is inversely related to freshwater availability (38).
Based on observation of national solid waste data, we expected
per capita waste generation by cities to be strongly correlated with
GDP (39, 40); a statistically significant upward trend was observed
(Table 1 and SI Appendix,Fig.S6), although the pattern of re-
siduals suggests other factors may be at play. Policies can matter; it
is interesting to contrast New York’s waste production (1.49 tons
per capita) with that of London (0.32 tons per capita), where the
share of municipal solid waste landfilled in the United Kingdom
has fallen from 80% in 2001 to 49% in 2010, encouraged by a
landfill tax (41). We also found the percentage growth rate in
GDP over 10 y to be correlated significantly with per capita waste
production. (Note, however, that this variable is insignificant when
correcting for multiple statistical inference; see Methods and SI
Appendix,Correction for Multiple Inferences).
Because concrete and steel largely become bound up in the
building stock in cities, we expected that their rates of consumption
would be higher for faster-growing cities. This expectation was
found to be the case for steel consumption (SI Appendix,Fig.S8).
We obtained data on steel consumption for only nine megacities
and found that steel consumption was correlated significantly with
the absolute population growth of megacities over 10 y (Table 1).
Data on cement consumption in 2011 were obtained for 10 cities;
five megacities—Mumbai,Kolkata,Delhi,Dhaka,andSaoPaulo—
were the largest consumers at 7.7–9.2 million tons. No significant
statistical correlations were found between cement and population
growth, GDP, or area per person.
Microscale Correlations. Although urbanized land area per person
correlates strongly with energy use in megacities at the macro-
level, it is a less significant factor in microscale analysis, as we
demonstrate by focusing on electricity use, for which building
floor area is an important underlying factor at the microscale.
We analyzed variables correlating with electricity use in subareas
of London and Buenos Aires.
Analysis of London boroughs demonstrates the significance of
gross floor area in explaining electricity use, with land area per
capita and income having weaker influence. Gross floor area data
for London’s boroughs were available only for industrial and
Table 1. Final regression results for factors correlating with
energy and material flows for megacities in 2011, correlations
with gross building floor area, and changes in energy use,
2001–2011
Variable t-stat (Pvalue); coefficient
Energy and material flows for 2011
Electricity consumption (R
2
=0.88; n=27; t
0,95
=2.056)
Urbanized area per person 13.55 (2.71 E−13); 21614
Heating and industrial fuel use (R
2
=0.85; n=27; t
0,95
=2.056)
HDD 5.87 (4.01 E−6); 0.02
Urbanized area per person 2.50 (0.02); 57722
Ground transportation fuels (R
2
=0.83; n=27; t
0,95
=2.056)
Urbanized area per person 11.40 (1.30 E−11); 92858
Water consumption (R
2
=0.78; n=27; t
0,95
=2.056)
Urbanized area per person 9.62 (4.75 E−10); 953201
Solid waste production (R
2
=0.87; n=20; t
0,95
=2.093)
GDP 5.98 (1.19 E−5); 7.41 E−6
10-y GDP growth rate, % 5.17 (6.40 E−5); 0.0002
Steel consumption (R
2
=0.88; n=9; t
0,95
=2.306)
10-y pop growth, no. of people 7.67 (5.93 E−5); 0.002
Regressions with gross building floor area
Urbanized area per person (R
2
=0.84; n=13; t
0,95
=2.179)
Total gross floor area 8.09 (3.36 E−6); 4.02 E−6
Urbanized area per person (R
2
=0.87; n=16; t
0,95
=2.131)
Residential gross floor area 9.84 (6.2 E−8); 7.47 E−6
Electricity consumption (R
2
=0.93; n=16; t
0,95
=2.131)
Residential gross floor area 14.05 (4.86 E−10); 0.19
Electricity consumption (R
2
=0.95; n=16; t
0,95
=2.131)
Residential gross floor area 3.66 (0.003); 0.12
Urbanized area per person 2.46 (0.03); 9726
Changes in energy flows, 2001–2011
Electricity, 10-y growth rate, % (R
2
=0.80; n=16; t
0,95
=2.131)
GDP, 10-y growth rate, % 7.80 (1.17 E−6); 0.56
Ground transportation, 10-y growth, % (R
2
=0.67; n=13; t
0,95
=
2.179)
GDP, 10-y growth rate, % 4.89 (0.0004); 0.61
Kennedy et al. PNAS Early Edition
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SUSTAINABILITY
SCIENCE
commercial buildings, and they show a strong correlation with in-
dustrial/commercial electricity use (Fig. 3A). Data on residential
land area per person (i.e., excluding commercial and industrial land
areas) were available for London, but they show a weak correlation
with residential electricity use per capita (Fig. 3B). Median house-
hold income also shows a weak correlation with electricity use per
capita (SI Appendix,Fig.S9). The correlation between income and
land area per capita shown at the macrolevel across megacities (SI
Appendix,Fig.S10and SI Appendix,Correction for Multiple In-
ferences) does not hold for the boroughs of London (SI Appendix,
Fig. S11), reflecting spatial variation in wealth and perhaps also
classic spatial tradeoffs between living space and disutility of travel.
In Buenos Aires, gross floor area data were not available for
the local municipalities; nonetheless, total electricity use (in resi-
dential, commercial, and industrial sectors combined) correlates
strongly with total building footprint areas for 24 local munici-
palities in the megacity (Fig. 3C). The annual residential electricity
use per person shows no relation to urbanized land area per
person (Fig. 3D).
The overall importance of gross building floor area in explaining
electricity use is seen further by linking it to the macroscale anal-
ysis. We were able to obtain or estimate values of residential gross
floor area and total gross floor area for 16 and 13 of the megacities,
respectively. Both measures show relatively strong correlations (R
2
=
0.87 and 0.84; Table 1) with urbanized land area per capita (SI
Appendix, Figs. S12 and S13). So, although cities can grow upwards,
more spread-out cities, with higher urbanized area per person have
more building floor area per person. Further statistical analysis
shows that residential gross floor area per person is highly corre-
lated with per capita electricity consumption in the megacities (R
2
=
0.93; Table 1). However, there are some nonbuilding uses of
electricity in cities, such as street lighting and public transit; hence
using both residential gross floor area per person and urbanized
land area per person gives a stronger model (R
2
=0.95; Table 1).
Low Consumption, High Consumption, and Efficient Use of Resources.
In addition to the assembled data on energy and material flows,
data on access to resources show that many of the megacities are
consuming resources at rates below those that support a basic
standard of living for all citizens. Substantial proportions of res-
idents in some megacities, particularly in South Asia, have no
access to basic services such as clean water, sewerage, electricity,
and formal waste disposal (SI Appendix, Table S2). For example,
SI Appendix, Fig. S14 shows that all the megacities with less than
100% access to grid electricity (except Shenzhen) are those with
annual electricity consumption below 2 MW per capita. The de-
velopment challenge for such poorer megacities entails increasing
rates of resource use above current low levels of consumption. The
challenge is complex, because there also are high rates of resource
wastage in some of these cities. For example, nonrevenue water use
is high in many megacities, reaching more than 70% of total water
consumption in Sao Paulo and Buenos Aires. Some of this loss may
be the result of informal/illegal water withdrawals; other losses
result from the poor state of infrastructure.
In contrast to the poorer megacities, some of the wealthier
megacities may have to decrease their levels of energy and ma-
terial consumption to reduce associated environmental impacts.
This situation is not straightforward, however: Not only do the
economies of cities have a bearing on their use of resources;
HDD, urban form, and growth rates also affect resource use, as
shown by our statistical analysis in Table 1. Nonetheless, the per
capita data do suggest opportunities for resource reduction. The
two United States megacities, for example, tend to be particularly
high in many resource categories, especially electricity, water, and
waste. Guangzhou also is a high-resource outlier with respect to
water consumption and heating and industrial fuel use. Water
efficiency is particularly low in Guangzhou, even compared with
the rest of Guangdong province, including Shenzhen. The center
of the city contains several industrial sites with outdated tech-
nology and high levels of water consumption; also, water prices are
very low in Guangzhou (42).
There are also examples among the wealthier cities of prac-
tices that have produced relatively high levels of resource effi-
ciency. For example, most of Moscow is serviced by a large district
heating system, which uses waste heat from electricity generation
to provide heating to most buildings in the city (see Moscow
United Energy Co., www.oaomoek.ru/ru/); Seoul has a wastewater
reuse system that saves on the input of water supplies; and Tokyo
has managed to reduce its water leakage rate to only 3% (43).
Among the wealthier cities overall, Paris is below the average
trend on many of the measures of resource flows.
Growth over Time. Rapid growth makes accessing resources
challenging in many megacities. Over the 10-y period ending in
2011, all the megacity populations in our study areas grew, and
more than half of them grew by more than 10% (SI Appendix,
Fig. S15). The fastest growth rates were in Istanbul, Dhaka, Bei-
jing, Shenzhen, and Shanghai, all of which grew by more than
40%. Most of the slower-growing populations were in high-income
Fig. 3. Microscale analysis of electricity use in London
and Buenos Aires. (A) Commercial electricity use in local
London boroughs is correlated with gross commercial
floor area (t-stat =18.85; Pvalue =3.69 E−17; R
2
=
0.90). (B) Residential electricity use in London boroughs
is weakly correlated with residential land area per
person (t-stat =3.34; Pvalue =0.0023; R
2
=0.28).
(C) Total electricity use in the local municipalities of
Buenos Aires is correlated with building footprint area
(t-stat =27.9; Pvalue =3.14E−19; R
2
=0.97). Data are
for 2011, excluding the central area, Ciudad de Buenos
Aires. (D) Annual residential electricity use per person
within the local municipalities of Buenos Aires has no
relation to urbanized land area per capita.
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megacities, such as New York City, Los Angeles, Paris, Tokyo,
and Osaka.
The resource flows for many of the megacities grew faster than
the rates of population growth. This difference is shown in Fig. 4 for
electricity and transportation fuel use in the megacities for which we
were able to determine 10-y growth rates. Six of the megacities had
increases in electricity consumption of 100% over the decade, and
in nine of them electricity use grew at more than three times the
population growth rate. Growth in transportation fuel use also was
three times the population growth in 7 of 15 megacities; growth in
transportation energy use was particularly high in the Chinese cities.
Further regression analysis shows that growth in electricity use
and transportation fuel use are significantly correlated with growth
in GDP (Table 1). Both of these energy flows are growing on av-
erage at about a half the rate of economic growth in megacities.
However, the rates of change in water use (SI Appendix,Fig.S16)
and solid waste production (SI Appendix,Fig.S17) are not corre-
lated significantly with GDP growth (Table 1). Also, one megacity,
London, notably managed to reduce its per capita electricity con-
sumption during the period 2001–2011 while growing its GDP.
Several factors may be responsible: a 66% rise in electricity prices,
improved energy efficiency in buildings and appliances, energy
labeling and increases in public awareness of the environmental
impacts of energy consumption, and a decline in manufacturing.
London is an exception, however. As the economies of megacities
continue to grow, the expectation under current trends is that their
energy use will continue to increase rapidly.
Conclusion
Overall energy and material flows vary considerably among
megacities. Rates between the lowest- and highest-consuming
megacities differ by a factor of 28 for energy per capita, 23 for
water per capita, 19 for waste production per capita, 35 for total
steel consumption, and 6 for total cement. Some megacities may
need to increase such resource flows to provide access to basic
services for all citizens, whereas others may aim to decrease energy
and material flows to reduce associated environmental impacts.
Policies that aim at resource efficiency can be successful, but the
energy and material flows of megacities also are influenced by
HDD, urban form, economic activity, and scale effects.
Our analysis has provided previously unidentified insights into
the relation between electricity consumption and urban form. The
close correlation between per capita electricity use and urbanized
area per capita at the macroscale is a consequence of the micro-
scale relationship between electricity use and building gross floor
area. Cities that have higher urbanized area per person have
more building floor area per person.
Methods
Data Collection and Quality Control. Use of the term “flow”in this study is
consistent with the stock and flows terminology used in national environmental
accounting [see Eurostat (44) or Brunner and Rechberger (45)]. In this study
“flows”refers to annual inputs or outputs of energy or material.
Energy and material flow data were collected for the 27 megacities using a
standard data-collection form described in ref. 20. After the data forms had
been returned by the network of researchers in the megacities, several steps
were taken to prepare the data for statistical analysis. First, all data were
entered systematically into a spreadsheet (see SI Appendix and Dataset S1 for
data). Attempts then were made to fill gaps in the reported data, especially
where the gaps were crucial to the analysis of resource and waste flows in
megacities. The number of data gaps was small; assumptions made to address
these gaps are detailed by each megacity in the SI Appendix (Part 3). Areas
deemed most critical were GDP, population density, HDD/CDD, stationary
energy use, transportation energy use, and solid waste disposal (for 2011).
The surveyed GDP data were cross-checked and supplemented with values
from The World Bank (46). All GDP values then were adjusted by a pur-
chasing power parity (PPP) conversion factor, defined as the number of local
currency units required to buy the same amounts of goods and services in
the local market that a US dollar would buy in the United States. PPP-adjusted
GDPs are standardized to an international dollar and therefore are amenable
to intercity comparison.
Population densities for most megacities in the analysis were acquired
from the World Bank (46). The exceptions were cases where the populations
considered in our study areas did not correspond well with those in the
World Bank’s data tables or for which data were missing; these were Cairo,
Dhaka, Lagos, Mexico City, Mumbai, Tehran, and the four Chinese mega-
cities. For these megacities we calculated the population density based on
data collected on our data forms.
HDD and CDD for each megacity were computed with online degree-day
calculators (www.degreedays.net) commonly used by building scientists. For
most megacities, the degree-day calculations were derived from standard air
temperature data observed at international airports. Given the rural or
semirural location of most airport observatories, the temperature data are
not representative of thermal conditions inside the city. In all cities, the
surface energy and radiation balances have been modified from the natural
state, and thus regional airport data are likely to underestimate the true
climatic differences that exist within and among megacities. However, be-
cause it is difficult to obtain air temperature data that are representative of
local climate conditions in megacities, regional airport data were used to
approximate urban-based temperatures.
All 27 climate stations in the megacities meet World Meteorological
Organization (WMO) standards and are qualified for use as synoptic-level
observatories. The online HDD calculator lists the airport and personal
weather stations near a particular city. For each of our 27 megacities, we
selected major international airport locations, because their data generally
are considered superior in quality to data from personal weather stations.
Each of the 27 airport stations has an International Civil Aviation Orga-
nization identifier code given by the International Civil Aviation Organi-
zation and listed by the online calculator. We cross-checked these codes
with the WMO station index numbers listed in the National Oceanic and
Atmospheric Administration climate database. In all 27 cases, our selected
stations had corresponding WMO index numbers. We verified the station
authenticity further for a few select stations in WMO Report No. 9 (“Ob-
serving Stations”) and found the stations are listed there too, with asso-
ciated metadata for station elevation, latitude and longitude coordinates,
observation schedules, and so forth.
Previous research (16) has shown that gasoline consumption in cities can
be estimated with an accuracy of about 5%, which may be a reasonable
estimate of the uncertainty in most of the energy and material flow data
collected. However, to provide a complete dataset for 2011, a few param-
eters (∼5%) were estimated based on national scale data. These exceptions
are detailed in the notes in SI Appendix,Definition and Notes on Megacities.
Total Resource Flows for Megacities. To quantify the total energy and material
flows for megacities (Figs. 1 and 2), we scaled the collected data by an
Fig. 4. Growth rates for electricity consumption (excluding line losses) (A) and ground transportation fuels (B), 2001–2011.
Kennedy et al. PNAS Early Edition
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SUSTAINABILITY
SCIENCE
adjustment factor based on Thomas Brinkhoff’s 2010 megacity populations
(SI Appendix, Fig. S2). Megacities whose study area populations fell below or
above those of Brinkhoff’s were adjusted by factors greater than or less than
unity. The purpose of this global adjustment to the data was to normalize
scale inconsistencies and uncertainties in survey reporting and to standard-
ize all flows to the spatial scale of a “megacity.”This adjustment is especially
pertinent because 77% of the megacities have a formal level of government
for the entire metropolitan area and its constituent cities. In some cases, e.g.,
Seoul and Mexico City, the study area population was smaller than that of
the full megacity but still included nearly 10 million people. In other cases,
e.g., Cairo and Lagos, the study area population was larger than that of the
megacity. For 14 of the 27 cities, the study area population was within 20%
of the megacity population defined by Brinkhoff (SI Appendix, Fig. S2). The
total population of the study areas was 410 million people, compared with
460 million for all megacities.
Note that that there is no single authoritative system for establishing megacity
boundaries. We used the Brinkhoff database as the basis for identifying the 27
megacities and establishing the approximate urban populations for data col-
lection, but the Brinkhoff populations are indicative numbers rather than au-
thoritative numbers. The most important consideration for this study was that we
obtained data for large metropolitan regions that contain substantial amounts of
the suburbs and hence avoided central city bias.
Analysis of Consistency with Scaling Laws. The scaling laws for cities have been
established by Bettencourt and colleagues (5, 6) by plotting large datasets on
log–log axes. We considered the 27 megacities at the top end of the collection
of all cities. Our simpler analysis entails calculating the total resource flows of
all of these megacities as a percentage of the world’stotalforcomparison
with the percentage of global population living in megacities. Quantities that
scale superlinearly should be consumed at disproportionally high rates by the
megacities, and quantities that scale sublinearly should be consumed at dis-
proportionally low rates. Our method is intended to check for consistency with
the scaling laws but is not a means of fitting parameters to the scaling laws.
The world totals used in the analysis are: populations, 6,892,319,000 (47);
global water consumption (∼2008), 2,600 km
3
/year (32); global waste dis-
posal, 3.93 million tons/day (48); global energy consumption, 393 exa joules
(www.iea.org/statistics); gasoline, 42,566,284 TJ (www.iea.org/statistics); electric-
ity, 18,396,735 GWh (www.iea.org/statistics); global GDP (2011), $77,200.00 US
billion PPP (49).
Regression Analysis. The statistical analysis of drivers of energy and material
flows (Table 1) was conducted using per capita values for the study areas (or
total consumption in the study area in the case of steel and cement); i.e., the
statistical analysis was conducted on the collected data without scaling. Two
related methods of analysis were undertaken. First, multiple regression was
undertaken using a stepwise process, starting with trial explanatory variables
selected from literature review and knowledge of urban systems and engi-
neering science. The initial models are given in SI Appendix,TableS1. Note that
in some cases the values of coefficients change substantially between the initial
and final models because statistically insignificant constants were eliminated.
ACKNOWLEDGMENTS. We thank Aleksander Sabic, Yi Lu, Siyuan Yang,
Eunhye Kim, Ruchira Ghosh, Dulcemaría Guerrero Sánchez, Pavel Moisseev,
Ahmed Kandil, O. Douglas Price, and Daniel Hoornweg for their help with
data collection and other aspects of the project. Funding for this project was
provided by the Enel Foundation.
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