Environ. Res. Lett. 14 (2019)045007 https://doi.org/10.1088/1748-9326/ab011f
LETTER
Spatially contextualized analysis of energy use for commuting
in India
Sohail Ahmad
1,2,4
and Felix Creutzig
2,3
1
Urban Studies, School of Social and Political Sciences, University of Glasgow, 40 Bute Gardens, Glasgow G12 8RS, United Kingdom
2
Mercator Research Institute on Global Commons andClimate Change, EUREF 19, D-10829 Berlin, Germany
3
Sustainability Economics of Human Settlements, Technical University Berlin, Germany
4
Author to whom any correspondence should be addressed.
Keywords: India, regression tree, GHG emissions, transport sector, mitigation, machine learning
Supplementary material for this article is available online
Abstract
India’s land transport GHG emissionsare small in international comparison, butgrowing exponentially.
Understandingofgeographically-specific determinants ofGHG emissions is crucial to devise low-
carbon sustainabledevelopmentstrategies. However, previous studies on transport patterns have been
limited to socio-economiccontext inlinear and stationary settings, and with limited spatial scope. Here,
we use a machine learning tool to develop a nested typology that categorizes all640 Indian districts
according to the econometrically identified drivers of their commuting emissions. Results reveal that per
capita commuting emissions significantly vary over space, after controlling for socioeconomic
characteristics, and are strongly influenced by built environment (e.g. urbanization, and road density),
and mobility-related variables (e.g. travel distance and travel modes). The commuting emissions of
districts are characterized by unique, place-specific combinations of drivers. We find that income and
urbanization are dominantclassifiers of commuting emissions, while we explain more fine-grained
patternswith mode choice andtravel distance.Surprisingly the most urbanized areaswith highest
population density are also associated with the highesttransport GHG emissions, a result that is
explained by high car ownership. This result contrasts with insights from OECD countries, where
commuting emissions are associated with low-density urban sprawl. Our findings demonstrate that
low-carbon commuting in India is best advanced with spatially differentiated strategies.
1. Introduction
The IPCC indicates that rapid reduction of greenhouse
gas (GHG)emissions is necessary to keep temperatures
below 2 °C(Edenhofer et al 2014). In 2010, over one-
fifth (∼23%, 6.7 GtCO
2
)of total energy-related emis-
sions originated in the transportation sector (Kahn
Ribeiro et al 2012, Sims et al 2014). Moreover,
transportation sector’s contributions to overall GHG
emissions are growing, both in absolute and relative
terms, as structural change shifts activity from indus-
try to service sectors (Schäfer 2005). Rapid decarboni-
zation is also challenged by the (perceived)high costs
of decarbonizing transport, requiring high energy-
density fuels (Creutzig 2016). Nonetheless, halving
CO
2
emission from transport by 2050 from 2010 levels
could be feasible, if not only electric two-, three- and
four-wheeler rapidly penetrate into motorized trans-
port markets, but if urbanization dynamics also shift
towards more compact urban form (Creutzig et al
2015).
Emerging and rapidly urbanizing countries, like
India, provide major opportunities to shape transport
systems and infrastructure around low-carbon
options (Shukla et al 2008, Bongardt et al 2013; Doll
et al 2013). These options have significant co-benefits,
such as reducing air pollutants (Xia et al 2015), enhan-
cing population health through physical activity
(Woodcock et al 2009,deSáet al 2017), energy security
(Dhar and Shukla 2015), and possibly alleviating pov-
erty (Starkey 2002). In particular, air pollution is a
major motivation, as 660 million Indians are esti-
mated to live in areas with health-unsecure levels air
fine particulate matter, reducing life expectance in
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averagy by more than 3 years (Greenstone et al 2015).
Often these co-benefits outweigh the benefits of dec-
arbonization in transportation’s sector (Creutzig and
He 2009, Schipper et al 2011, World Health Organiza-
tion 2011, Creutzig et al 2012).
India’s motorized vehicle growth has increased
exponentially over time, and is dominated by two-
wheeler (figure 1(a)) that led to exponential increase in
the road transport sector’sGHGemissions(figure 1(b)).
Dhar et al (2018)estimated that Indian transport sector
energy demand would increase by 4.5 times, 2.7 times,
2.4 times, and 1.7 times in Business As Usual, Nationally
Determined Contributions, 2 °C, and 1.5 °Cscenarios
respectively by 2050 compared to 2015 levels. Notably,
Dhar et al suggest that deep decorbonization in the trans-
port sector, such as envisaged in 2 °Cor1.5°C scenarios,
will require both demand and supply side policy inter-
ventions, including transformative human behaviors
relying on information technology, internet and the
sharing economy, the electrification of the transport sec-
tor, and innovations in national and sectoral policies,
including decarbonization of electricities and explicit
carbon prices.
Given the high and growing share of carbon emis-
sions from the transport sector, several studies have been
conducted to deepen the understanding on transport-
based GHG emissions, particularly, its measurements
and compositions, geographic/spatial variations, and
determinants or correlates. Major transport-based stu-
dies on GHG emissions used aggregate level assessment,
as those from the International Energy Agency’sstudies
(IEA 2009), and integrated assessment modelling
(Edelenbosch et al 2017,Dharet al 2018).Studiesusing
bottom-up approach utilized disaggregated GHG
emissions, such as activity-based (e.g. work, leisure)
(Millard‐Ball and Schipper 2011, Jones and Kammen
2014), or mobile sources based (e.g. road, aviation, rail-
ways, and navigation)(MoEF 2010).Geographic/spatial
Figure 1. (a)Growth trend in registered motor vehicles, 1990–2016. (b)Estimated road transport sector emissions in India,
1990–2013. Four-wheeler includes cars, jeeps and taxis. Trend analyses show exponential growth in vehicles and related emissions
over time. Source: (a)Ministry of Road Transport & Highways MORTH (2018);(b)from various studies, as cited in Singh et al (2019).
2
Environ. Res. Lett. 14 (2019)045007
variations of transport-based GHG emissions are mostly
focused on region and country (Streets et al 2003,
IEA 2009,MoEF2010).Afewstudies,butgrowingin
number, investigated transport-based GHG emissions at
subnational level, including the state level (Ramachandra
and Shwetmala 2009),thecitylevel(Ahmad et al 2015)
and the sub-city level (Wang et al 2017).Studieshavealso
identified and quantified determinants of GHG emis-
sions at individual or household level (Ahmad et al
2015,2017), and often spatially aggregated level (Marco-
tullio et al 2012,Guoet al 2014). Other studies measured
vehicle miles travelled (Cervero and Murakami 2010),
person miles travelled (Krizek 2003,Jainet al 2018,
Korzhenevych and Jain 2018), or transport expenditure
(Ahmad et al 2016), proxies of transport-based GHG
emissions. While these studies provide valuable insights
about correlates of transport-based GHG emissions, one
of the characteristics features of these studies is the use of
aspatial (stationary)analysismethodssuchasmulti-
variate regression that do not allow for variations of coef-
ficients over space.
Given significant socio-spatial variation across
Indian districts, we hypothesize that major determi-
nants of transportation emissions vary widely over
space. This study aims (a)to understand spatial pat-
tern/typology of commuting GHG emissions in India,
and (b)to identify its correlates in spatial context (dis-
trict level). To address these issues, we investigate spa-
tially explicit data of commuting patterns employing
tree regression to identify typology of commuting
GHG emissions.
2. Methods
We describe first the regression model linking com-
muting emissions with it determinants, and then the
recursive partitions method used to identify the
different types of districts (each of which is subject to a
separate regression). Throughout the process, we also
test and validate models, wherever needed.
We start our analysis of the determinants of commut-
ing emissions with the standard regression equation:
å
bb e=+ + ()YXln ln , 1
j
K
kjk j0
where,
Y
j
is commuting emissions for district j,
X
jk
are
determinants factors, and
e
jis the classical error term
representing the effects of unobserved variables.
Determinants factors consist of built environment
(urbanization level, travel time to nearest city, popula-
tion density, and road density), mobility related
variables (travel distance, travel modes, and fuel
prices), and socio-economic characteristics (GDP, and
literacy rate). Four variables—commuting emissions,
population density, road density, GDP—are consid-
ered in their logarithmic transform respecting the
distribution of data, and following the econometric
literature on this topic (Ahmad et al 2015, Baiocchi
et al 2015).
For developing typology of districts with respect to
drivers of commuting emissions, we use the classifica-
tion and regression tree (CART)methods developed
by Breiman et al (1984), that iteratively partitions the
data into homogeneous subgroups, by fitting separate
regression model at each node (equation (1)). We use a
regression tree approach since we want to predict the
values of a continuous variable, commuting emis-
sions, in different spatial and socio-economic con-
texts. The algorithm of CART is structured as a
sequence of questions, which resulted into a tree like
structure, where the ends are terminal nodes that cor-
respond to types of commuting patterns according to
spatial context. CART has three main elements: rules
for splitting data at a node based on the values of one
variable; stopping rules of splitting; and prediction for
the target variable in each terminal node. At each split,
the available sample is partitioned into two groups by
maximizing an information measure of node homo-
geneity and selecting the covariate showing the best
split. The split can be presented, as a binary decision
tree where the branch on the right of each non-term-
inal node contains the districts for which split variable
is greater than the split value. CART provides compu-
tationally efficient strategies for estimating non-para-
metric regression model (for detail discussion see
Baiocchi et al (2015)).
To avoid overfitting, a large tree is grown first and
then reduced in size by a pruning process. Given the
flexible non-parametric approach, it is possible to fita
tree with many parameters, including noisy features,
which may render to some degree arbitrary and unsui-
table for generalization and interpretation. Here, we
hence improve the predictive ability of a tree of a spe-
cific size by using a technique known as cross-valida-
tion. Tree size is optimized by minimizing the cross-
validated error.
Alternatively, we have also used geographically
weighted regression to assess determinants of com-
muting emissions spatially to validate our overall find-
ings from the tree regressions (see SI is available online
at stacks.iop.org/ERL/14/045007/mmedia). In gen-
eral findings from both methods agree on the key find-
ings. However, we chose tree regression as the main
method for this paper as tree regression allows for rela-
tively straight-forward interpretation and enables the
construction of policy-relevant typologies. These ana-
lyses were performed using R, a free programming
language and software environment for statistical
computing and graphics.
2.1. Data
The commuting data were taken from the Census of
India enumeration on ‘other workers by distance from
residence to place of work and mode of travel to place
of work’(Census of India 2011a). Here ‘other workers’
are those persons whose main activity was ascertained
according as their time spent as a worker producing
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Environ. Res. Lett. 14 (2019)045007
goods and services or as a non-worker other than those
(a)working as cultivator, (b)working as agricultural
labourer, and (c)working at household industry. This
commuting data is disaggregated by location (urban
and rural), gender (male and female), and distance
ranges at district level. Travel mode shares related data
include walk, bicycle, two-wheeler, four-wheeler,
three-wheeler, bus, train, and water transport or their
groups, which are active transport (walk and cycle),
motorized transport-private (two-wheeler and four-
wheeler), and motorized transport-public (three-
wheeler, bus, train, and water transport). We have
used this data for calculating annual per capita
commuting emissions (kg CO
2
/p/yr)as follows:
å
=´
´
´
⎡
⎣
⎤
⎦
()
()
2
YAvEmissions 2 . Commuting distance
Emission factor
300 d
District Pop ,
jij
j
,
where irepresents mean daily distance ranges in
kilometer (e.g. 0.5 km for 0–1 km range; 3.5 km for
2–5 km range; 8 km for 6–10 km range; 15.5 km for
11–20 km range; 25.5 km for 21–30 km range;
40.5 km for 31–50 km range; and 60.5 km for
51+km range), and jrepresents transportation
modes (e.g. two-wheeler, bus). To represent the return
trip, emissions were multiplied by 2. Values were
converted to annual emissions by assuming 300 mean
working days. Further divided by district population
to calculate per person emissions. Emission factor for
travel mode were taken from data of the India GHG
Program (2014)in kg CO
2
/km (or kg
CO
2
/passenger-km for pblic transport modes such as
bus)(see table S1).
Major explanatory variables data were extracted
from publicly available sources (table 1). Road density,
for instance, is calculated from the road network data
from the Open Street Map, a collaborative project to
generate a free map of the world based on crowed-
source data (openstreetmap.org).
3. Results
India’s mean annual commuting emissions (home to/
from work)is 20 kg CO
2
per capita, with the highest
(140 kg CO
2
)in Gurgaon district (Haryana)and the
lowest (1.8 kg CO
2
)in Shrawasti district (Uttar Pradesh)
(table 1and figure S1). The mean urbanization level is
26.4%, but varies immensely from null (e.g. Kinnaur
district, Himachal Pradesh)to 100% urban population
(e.g. Mumbai district, Maharashtra). The average travel
Table 1. Descriptive summary of the study population India, 2011.
Variable Mean St. Dev. Min Max Data source
Dependent variable:
Commuting emissions, kg CO
2
/person/year 20.0 18.9 1.8 140.4 Authors’calculation based on Census of India
(2011a)
Independent variables:
Built environment
Urbanization level, % 26.4 21.1 0.0 100.0 Census of India (2011b)
Travel time nearest city, min 68.7 137.9 0.0 1,169.3 Authors’calculation from Weiss et al (2018)
Population density, persons/km
2
934.4 3,025.3 1 37,346 Census of India (2011b)
Road density, km/km
2
0.7 1.9 0.02 22.8 Authors’calculation from the Open Street
Map (June)2017
Socio economic characteristics
GDP, ₹/cap 38 830 30 197 5,198 411 633 State’s planning department (c.2011)
Literacy rate, % 72.3 10.5 36.1 97.9 Census of India (2011b)
Mobility-related variables
Commuting distance, km 5.9 1.9 1.3 14.0 Census of India (2011a)
Walk share, % 13.2 8.3 3.1 57.3 -do-
Bicycle share, % 14.4 10.0 0.4 45.8 -do-
Active transport, % 27.6 11.8 5.6 68.3 -do-
Three-wheeler share, % 4.6 3.5 0.4 24.5 -do-
Bus share, % 31.6 16.7 0.6 74.2 -do-
Train share, % 11.2 11.2 0.01 70.1 -do-
Water transp. share, % 0.7 2.4 0.0 36.9 -do-
Motorized transport-public, % 48.1 15.5 7.3 85.5 -do-
Two-wheeler share, % 15.9 7.9 0.7 41.6 -do-
Four-wheeler share, % 6.3 6.3 0.5 46.5 -do-
Motorized transport-private, % 22.2 9.2 3.5 49.5 -do-
Diesel price, ₹/l 46.2 1.7 42.2 48.6 Ministry of Petroleum & Natural Gas, as cited
in Lok Sabha Secretariate (2013)
Valid N640
Note: 1 US$=44.7 US$ on 31 March 2011.
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Environ. Res. Lett. 14 (2019)045007
time to the nearest urban center is about 69 min, but it
can be as high as 20 h for inhabitants in remote disctricts.
The mean district density is 934 p km
−2
(SD=
3025 p km
−2
), with the highest 37 000 p km
−2
in North
East district (Delhi), and the lowest one p km
−2
in
Dibang Valley district (Arunachal Pradesh).Theroad
density varies between 0.02 and 22.8 km km
−2
,with
mean 0.7 km km
−2
. The mean district GDP per capita is
39 000 ₹(circa 2011), with a minimum of 5200 ₹in
Sheohar district (Bihar)and a maximum of 411 000 ₹in
Mumbai district (Maharashtra). The mean commuting
distance (among commuters)is 5.9 km, with the lowest
1.3 km in Longleng district (Nagaland)and the highest
14 km in Dharmapuri district (Tamil Nadu). Roughly,
annual per capita commuting mobility (mean travel
distance/day=9.007 km)is about 58% of per capita
passenger mobility 5685 km (Dhar and Shukla 2015).
There is a huge variation in travel mode choices in
Indian districts, for instance, four-wheeler share
between 0.5% and 47% and active transport (walking
and cycling)share between 6% and 68%. In the
following, wepresent seven insights ofour analyses.
First, income and urbanization are the key drivers
of the district typology with respect to commuting
Figure 2. District types in India as determined by their Commuting CO
2
emissions drivers. Key statistics are given for each type in the
table below (% values are rounded to the nearest whole number, GDPvalues rounded to the nearest thosand).CO
2
emissions drivers
split districts recursively to produce maximally distinct district types. Rectangles indicate the splitting criteria in terms of splitting
variable and threshold value of splitting variable; Ovals are terminal nodes which represent the different district types and contain the
estimated subsamples (see figure 3). Values inside the rectangles or ovals represents average commuting emissions for respective type/
node in kg CO
2
. Small square above rectangles or ovals represent node number, figure 3maps final nodes that are in oval shape. N
represents number of districts at that node and parallel figure in % represents percentage of total districts.
5
Environ. Res. Lett. 14 (2019)045007
emissions (figure 2). Income is the best discriminator
for a typology of districts with respect to commuting
emissions. The split based on income occurs at the
threshold of about 28 000 ₹/capita. In the high-
income part of the tree (nodes 12, 13, 14, and 15)urba-
nization is the dominant discriminatory attribute
splitting clusters at about 43% level. However, urbani-
zation is not a discriminator in low-income district
types.
Second, average commuting emissions are highest
for districts with high-income inhabitants, that are
highly-urbanized, and that heavily rely on four-
wheeler for commuting (node 15), and lowest for
districts with low-income, have shorter commuting
distance, and rely least on three-wheeler for commut-
ing (node 8). These patterns contrast with observa-
tions from countries like the United States, where
commuting emissions are highest in low-dense settle-
ments (suburban or rural)(Grubler et al 2012, Jones
and Kammen 2014). Within high-income and highly-
urbanized districts, reduced reliance on four-wheeler
for commuting (<20%)cuts average commuting
emissions by 60% from 89 kg CO
2
(node 15)to 36 kg
CO
2
(node 14). Similarly, within low-income districts,
shorter commuting distance (<5.7 km)cuts average
commuting emissions by 51%, 12 kg CO
2
(node 5)to
5.9 kg CO
2
(node 4), and within low-income, and
shorter commuting district, reduced reliance on
three-wheeler (<6.1%)cuts average commuting emis-
sions by 58%, 13 kg CO
2
(node 9)to 5.4 kg CO
2
(node 8).
Third, commuting emissions drivers’impact is not
homogeneous, but context dependent (figure 2and
table S3). Thus emission drivers for commuting can-
not be adequately explained by a unique global model
(table S2), as also argued by Baiocchi et al (2015)for
residential CO
2
emissions in England. The impact of
urbanization level on commuting emissions shows
strong variability over the study area in expected posi-
tive directionality. A one percentage point increase in
urbanization is associated with increase in commuting
emissions between 0.5% (node 14)and 2% (node 5).
Similarly, income has spatially varying influence in
increasing commuting emissions: a 1% increase in
income is associated with increase in commuting
emissions between 0.35% (node 8)and 0.40%
Figure 3. Human settlement type, as characterized by its commuting emission drivers in India. Each district is colored according the
corresponding node from the tree regression results it belongs to earlier figure 2.
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Environ. Res. Lett. 14 (2019)045007
(node 5). Commuting emissions also increase with
road density; a 1% increase in road density is asso-
ciated with increase in commuting emissions between
0.07% (node 14)and 0.24% (node 8). The impact of
commuting distance on commuting emissions shows
again strong variability in expected positive direction-
ality. A 1 km increase in commuting distance could
increase commuting emissions between 4.3% (node
13)and 20.5% (node 12). These heterogeneous rela-
tionships indicate that most of the explanatory vari-
ables have higher magnitude of influence in currently
low emitting districts/regions (nodes 8, 9 and 5), for
instance, urbanization, population density, road den-
sity, and GDP.
As expected fuel price is negatively associated with
commuting emissions, but only in low-income
districts (table S2)or specifically in Node 9 and 12
(table S3). With a 1 ₹increase in diesel price, commut-
ing emissions decrease by 11% in node 9, and 10% in
node 8 (table S3), whereas aggregate 3% in low-
income districts (table S2). Given these districts have
least commuting emissions and low socio-economic
status (figure 2), our study finds limited support for
increasing gasoline prices as a strategy to mitigate
commuting emissions. Rather increasing gasoline
price would burden mobility among low socio-
economic status’population. However, increasing
transport fuel prices in Indian metropolitan areas has
been identified as a strategy to improve public health
(Ahmad et al 2017).
Fourth, per capita commuting emissions decrease
with increase in population density, except in node 5
where commuting emissions increase with increase in
population density, but with lesser statistical sig-
nificance (p<0.1)(table S3). On average, a 10%
increase in densification reduces commuting emis-
sions by 1.1%, ceteris paribus. Figure 4reveals residents
living in dense areas (mostly among metropolitan
regions)are affluent and that contribute to higher per
capita commuting emissions. However, there are sev-
eral regions (e.g. Gurgaon and Faridabad)that have
high per capita commuting emissions but relatively
low population density. Mostly, these regions fall in
nodes 14 and 15 that have high motorized transport
share as well as high road density, hence road conges-
tion (figure 2). This suggests increasing population
density with appropriate transportation systems (e.g.
active transport and public transport)could reduce
commuting emissions in a few regions, mostly across
metropolitan regions.
Fifth, the mean per capita commuting emissions of
the district typologies vary by a factor of 16.5. Variance
in per capita commuting emissions is higher for high-
income districts (factor of 6)than for low-income dis-
tricts (factor of 2.5). This could be partially explained
by the variation in income, urbanization level and
Figure 4. Comuting emissions per capita increases with increasing economic activity. Residents living in higher density regions are
very affuluent, and have higher per capita commuting emissions (e.g. metropolitan regions). However, there are several regions with
low density but higher per capita commuting emissions (e.g. Faridabad, Gurgaon, North Goa, and South Goa). Scatterplot smoothing
uses local regression method ‘loess’. The National Capital Territory of Delhi hasfollowing districts (mostly labelled): Central, North,
South, East, North East, South West, New Delhi, North West and West.
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Environ. Res. Lett. 14 (2019)045007
transport mode choices between high- and low-
income district typologies (see figures 2,4, and 5).
Importantly, districts with similar emissions may have
emissions driven by a different set of determinants.
For example, nodes 5, 9 and 12 have similar average
emissions (12, 13, and 13 kg CO
2
/cap respectively).
However, node 5 is characterized by low income, and
long distances, node 9 by low income, short distances,
and a high number of three-wheeler, and node 12 by
high income, low urbanization, and low road density
(figure 2). This result emphasizes the importance of
understanding location-specific determinants, even
when emission levels are similar, to derive location-
specific policies.
Sixth, among all Indian megacities, Delhi National
Capital Region (hereafter Delhi)has the highest com-
muting emissions per capita (part of node 15). Node
15, which includes Delhi’s region, has 2.5 times higher
commuting emissions than node 14, which includes
most other megacities—Mumbai, Kolkata, Chennai,
Bangalore, and Hyderabad. Delhi’s higher socio-eco-
nomic status and heavy reliance on private travel
modes (figures 2and 5)led to higher commuting
emissions than in other megacities. This may also be
an effect of being the center of government; similarly,
as capital of China, Beijing’s emissions from car trans-
port exceed those of Shanghai (Liu et al 2007, Creutzig
and He 2009). Delhi is also one of the most air-polluted
cities in India. This suggests that implementing
sustainable transportation options should have higher
priority in Delhi than in other megacities.
Seventh, the same district types tend to cluster spa-
tially, as district typology map (figure 3)as well as
commuting emissions distribution map (figure S1)
reveal. Districts of the same type cluster demonstrates
that features covary spatially. The effects is due to
underlying drivers vary spatially, e.g. urbanization
level. This finding has policy-relevant implication, for
instance, adopting strategy from one place to another.
4. Discussion and conclusion
This study provides an improved understanding of
commuting emissions in spatial context. To the best of
our knowledge, this is the first attempt to assess India’s
commuting emissions patterns and its drivers at
district level (n=640). Our results provide spatial
information relevant to sustainable transport policies
at district/regional levels.
Our analysis reveals that GHG emissions from com-
muting are grounded in urbanization, socio-economic
characteristics, and travel mode choices. This result is in
accordance with previous research. For instance, pre-
vious studies reveal a 1% rise in urbanization increases
road transport energy use by 0.37% (Poumanyvong et al
2012)and CO
2
emissions by 0.30% (Poumanyvong and
Kaneko 2010)inthemiddleincomecounries.Similarly,
Figure 5. Urbanization and GDP per capita in reference to node levels commuting emissions in India, 2011. Vertical dashed line
(1)represents first split by income and horizontal dashed line (2)represents second split by urbanization level in high income districts
as a result of tree regressions. Nodes are the same as in figures 2–5.
8
Environ. Res. Lett. 14 (2019)045007
Zhang and Lin (2012)find that in China a 1% increase in
urbanization is associated with 0.12% increase in CO
2
emissions. Similarly, our estimate reveals that urbaniza-
tion is positively associated with commuting emissions
(urbanization-commuting emission elasticity value is
0.24, that is a 1% increase in urbanization correlates with
a 0.24% increase in commuting emissions).Thesitua-
tion in OECD countries is different. For example, Elliott
and Clement (2014)showed that per capita CO
2
emis-
sions was negatively associated with urbanization and
density at county level in USA, after considering con-
stituents of urbanization (i.e. density, percentage of
developed land, and urban hierarchy). Urbanization is
also associated with denser built-environment, and the-
ory suggests that commuting distances are shorter and
more public transit is used, resulting in lower emissions
(Fujita 1989,Ahmadet al 2013,Creutzig2014), in agree-
ment with global data analysis of cities and their energy
use and GHG emissions (Marcotullio et al 2013,Creutzig
et al 2015, Lohrey and Creutzig 2016). In contrast, our
analysis reveals that transport emissions are highest in
some of the dense urban areas (figure 4). Districts with
similar population density, however, significantly vary in
per capita commuting emissions (seenode14in
figure 4). These results indicate that simple-minded den-
sification is an inappropriate policy for reducing com-
muters’GHG emissions. Instead, a focus on electric two,
three, or four-wheeler, and efficientpublictransit,e.g.in
terms of Bus Rapid Transit systems is warranted, like
Ahmedabad and Bhopal.
Notably, e-rickshaws are rapidly emerging in
metropolitans’suburbs and secondary cities in India,
even though they are hardly supported by subsidies
(Altenburg et al 2012, Ward and Upadhyay 2018). This
suggests that India has the potential to leapfrog oil-dri-
ven mobility to electric mobility. Indeed, expansion
of e-vehicles would reduce both emissions and
air-pollutions, particularly in suburbs and secondary
cities (e.g. node 9). However, related infrastructures
need appropriate investments for public charging
infrastructures in simultaneously and in a coordinated
way (Altenburg et al 2012). With such investments,
e-vehicle could also become a viable option for long-
distance commuting.
Nonetheless, significant variation in commuting
emission drivers over space suggests that one-solution
fits-all for mitigating transport sector emissions will not
work.Solutionsinsteadneedtobetailoredtogeo-
graphical contexts (table 2). Finer spatial clustering of
determinants of commuting emissions enables both spe-
cialization and generalization of policies. Policy interven-
tions can be targeted to the district or region level,
acknowledging their different combinations of commut-
ing emission drivers; in turn, policies can also be general-
ized to similar district/region. Across similar regions,
policy makers can learn from context-specificbestprac-
tice experiences. At a local scale, our analyses enable
nuances to be understood by highlighting the spatial het-
erogeneity of the relationships. For instance, variables’
coefficients significantly vary over space (tables S2 and
S3). Therefore, a similar change in built environment or
mobility-related variables may have different response in
mitigating commuting emissions across the country.
Another striking example is Delhi’s significantly
large commuting emissions than other metropolitan
cities, associated with high-income, a high share of
four-wheeler (node 15). Unlike other districts on the
same node, Delhi (and its region)has vast population
(over 46 million), and one of the most polluted regions
in the world (Ahmad et al 2013). Our analysis
suggests immediate policy interventions to mitigate
commuting emissions in the region, particularly through
alternative commuting modes (non-motorized, e-bikes),
also as feeders to improved private transport, as also
Table 2. Summary of district traits and potential interventions by nodes for mitigating commuting emissions.
Node (Av. Emiss.)Trait of region
a
Potential interventions
8(5.4 kg)Low-income, short commuting distance, and
less use of three-wheeler
Improving public transport infrastructure
9(12.5 kg)Low-income, short commuting distance, but
high use three-wheeler
Densification and taxes on fuels
5(12.1 kg)Low income, but long commuting distance Reducing commuting distance (for instance through mixed land
use)and improving active/public transport infrastructure
12 (12.9 kg)High-income, less urbanized with lower road
density
Reducing commuting distance, taxes on fuel, and improving active/
pulic transport infrastructure
13 (20.4 kg)High-income, less urbanized but higher road
density (e.g. Nellor)
Densification, reducing commuting distance, and improving active
transport infrastructure
14 (35.6 kg)High-income, high-urbanized, with less car
use for commuting (e.g. Mumbai)
Reducing commuting distance, and discouraging private transport
use, possibly taxes on fuel
b
15 (89.4 kg)High-income, high-urbanized, and high car
use for commuting (e.g. Delhi)
Densification, reducing commuting distance, and improving public
transport infrastructure, possibly taxes on fuel
b
Note:
a
See figure 2for cut-off line, and figure 3for geographical location.
b
Here we suggest fuel taxes based on related study (Ahmad et al 2017).
9
Environ. Res. Lett. 14 (2019)045007
echoed while studying Delhi’slandcoverchange
(Ahmad et al 2016). Rapid action on both demand and
supply sides would avoid not only the worst aspects of a
collapsing mobility system, but would also support low-
carbon trajectories (Creutzig and He 2009,Dharet al
2018). Delhi, in fact, can emerge as a laboratory for
experimenting sustainable options, which may be repli-
cated in other regions.
This study has two limitations: (a)adoption of a
modelling approach at district level using aggregated
data, whereas microdata (e.g. individual or household)
and high spatial resolution (e.g. lower administrative unit
than district)could provide a better estimate; (b)census
data has home to/from work commuting with travelling
modes and distance ranges only. New types of data are
necessary to improve these types of studies. For next cen-
sus,wesuggesttocollectinformationontriplengthsand
reason and vehicle load factor. Unavailability of India’s
national travel survey, these additional information
could be useful for better assessments.
Our new commuting emissions estimate over
space has several implications for urban policymakers.
The spatial estimate identifies hotspots for imple-
menting low-carbon commuting options, through
restructuring travel characteristics (e.g. travel mode
shift, and travel distance)and modifying the built
environment (e.g. urbanization, and density aspects).
As a result, we gain an improved understanding of the
transport sector’s mitigation options in spatial con-
text. This will be useful for multi-level of governments
to deduce transport policies and programs in local or
regional context as per their priorities.
The nonlinear, non-stationarity understanding of
India’s commuting patterns is scalable also to other
issues and other world regions. Our results suggest
that policies (e.g. to decrease emissions or improve
active transportation)should follow the spatial pat-
terns of the relationships to strengthen their efficiency.
Conceptually, our analyses highlights that the study of
commuting emissions should be not only socio-eco-
nomic specific but also location specific. Therefore, we
argue for making best use of the increasing availability
of big data sources for identifying context-based strati-
fied sustainable solutions.
Acknowledgments
Earlier versions of this article were presented in the Cities
and Climate Change Science Conference (Edmonton
2018), Systematizing and upscaling urban solutions
for climate change mitigation (SUUCCM)conference
(Berlin 2018), and Humboldt-Kolleg ‘Sustainable devel-
opment and climate change: Connecting research, educa-
tion, policy and practice’(Belgrade 2018).Theauthors
gratefully acknowledge the comments and suggestions
received on these occasions. Authors acknowledge Ulf
Weddige for excellent data support in the project, and
Sumit Mishra for suggesting/providing relevant data and
maps. SA is thankful to Jérôme Hilaire, and Max
Callaghan for their valuable help in navigating spatial data
in ArcGIS/R, Anjali Ramakrishnan for her constructive
feedback on an earlier draft, and Vidhi Singh for data
assistance. SA acknowledges the Alexander von Hum-
boldt Foundation and the Federal Ministry for Education
and Research (Germany)for the research fellowship.
ORCID iDs
Sohail Ahmad https://orcid.org/0000-0002-
2816-8484
Felix Creutzig https://orcid.org/0000-0002-5710-3348
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