Cities as engines of economic growth: The case for providing basic infrastructure and services in urban areas

Full text

Working Paper
October 2016
Urban; Energy
Keywords:
Cities, climate mitigation, electricity
access, urban poverty, economics
Cities as engines
of economic
growth
The case for providing basic
infrastructure and services in
urban areas
Sarah Colenbrander

International Institute for Environment and Development
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About the authors
Sarah Colenbrander, Researcher, Human Settlements Group.
E-mail: sarah.colenbrander@iied.org
Produced by IIED’s Human Settlements
Group
The Human Settlements Group works to reduce poverty and
improve health and housing conditions in the urban centres of
Africa, Asia and Latin America. It seeks to combine this with
promoting good governance and more ecologically sustainable
patterns of urban development and rural-urban linkages
Acknowledgements
Warm thanks to Stephanie Ray, who was responsible for the
data visualisations in this paper, and to David Satterthwaite,
David Dodman and Andy Norton for their constructive
comments on the text. I am particularly grateful to the following
people for reviewing the quantitative analysis as well as the
draft paper:
• Sarah Lester, Senior Energy Advisor, Practical Action; DPhil
Researcher, University of Oxford.
• Andrew Sudmant, Research Fellow, University of Leeds;
Associate, ESRC Centre for Climate Change Economics and
Policy.
• Anthony McDonnell, Head of Economic Research,
Independent Government Review on Antimicrobial
Resistance, Wellcome Trust.
Published by IIED, October 2016
Colenbrander, S. 2016. Cities as engines of economic growth:
the case for providing basic infrastructure and services in urban
areas
.
IIED Working Paper. IIED, London.
http://pubs.iied.org/10801IIED
IS BN 978-1-78 431-405-7
Printed on recycled paper with vegetable-based inks.

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Urbanisation offers substantial opportunities to
reduce poverty, in part because it is more cost-
effective to meet many basic needs in cities than in
rural areas. This paper demonstrates that providing
electricity to the 200 million urban residents who
currently lack access would require only $1.37
billion per year to 2045. Generating this electricity
from low-carbon options (consistent with avoiding
a 2°C temperature rise) would cost only 1% more.
This demonstrates that relatively small amounts of
resources need to be mobilised to deliver basic
services and infrastructure to the urban poor –
an essential precursor to inclusive and sustained
economic growth.
Contents
Summary 4
1 Introduction 5
2 Methods 7
Limitations 8
3 Results 9
Current levels of electricity access 9
Economics of urban electricity access 11
Case study: South Africa 15
Case study: India 17
4 Discussion 19
Appendix 1: Data sources and assumptions 22
Africa (excluding South Africa) 22
Asia (excluding India) 23
India 24
Latin America 25
Middle East 26
South Africa 26
References 28
Related reading 31

Cities as e ngin es of eConomiC growth | The case for providing basic infrasTrucTure and services in urban areas
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Summary
Cities have often been described as engines of
economic growth, but neither all cities, nor all residents
within a given city, necessarily benefit from the potential
dividends of urbanisation. This is evident by the fact
that one in seven of the world’s people live in poverty
in urban areas. The urban poor often lack access to
basic infrastructure and services, which substantially
reduces their wellbeing, resilience and productivity. Yet
urbanisation offers substantial opportunities to reduce
poverty, not least because it is more cost-effective
to meet many basic needs in urban areas than it is in
rural ones.
Focusing on the electricity sector, this paper
demonstrates that the investment needs associated
with providing basic infrastructure and services to
un-served urban populations are small relative to their
benefits. Nearly 200 million urban residents, primarily in
sub-Saharan Africa, currently lack access to electricity.
Providing them all with a basic level of electricity access
– enough to power two light bulbs, two mobile phones
and a couple of small appliances – would cost $1.37
billion per year to 2045. Generating this electricity
from low-carbon options (consistent with avoiding a
global average temperature rise of more than 2°C)
would cost $1.38 billion per year – less than 1 per
cent more than using conventional technologies. These
estimates include the capital, operating, maintenance
and financing costs of the generation, transmission and
distribution infrastructure. This equates to less than 0.03
per cent of the world’s annual fossil fuel subsidies (as
estimated by the International Monetary Fund in 2015).
This global analysis illustrates that relatively small
amounts of finance are needed to provide basic
services and infrastructure to the urban poor. However,
mobilising these resources depends on the political
will to introduce enabling policies and local capacities
to make the infrastructure investments. In the absence
of the necessary commitment and capacities, rising
inequality in urban areas is likely to constrain economic
growth by limiting the productivity of low-income
groups. This suggests that cities can only achieve their
potential to be engines of economic growth in the long-
term if they prioritise poverty reduction in the near-term.

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1
Introduction
Cities are engines of economic growth. This idea
has captured the imagination of decision-makers
for decades, from the seminal report Urban Policy
and Economic Development: An Agenda for the
199 0s (World Bank, 1991) to more recent work
by the Commission on Growth and Development
(Duranton, 2008; Spence, et al., 2009) and the Global
Commission for the Economy and Climate (Floater,
et al., 2014; Gouldson, et al., 2015). The rapid economic
growth usually associated with urbanisation can be
partially attributed to structural transformation, as
labour moves from the agricultural sector to industry
and services. It can also be attributed to agglomeration
and scale economies, as proximity and density reduce
the per capita costs of providing infrastructure and
services, as well as creating knowledge spillovers and
specialisation that hugely enhance the productivity of
urban residents.
However, neither all cities, nor all residents within
a given city, necessarily benefit from the potential
economic dividends of urbanisation. This is evident by
the fact that one in seven of the world’s population live
in poverty in urban areas. Even in cities with a high per
capita GDP, many urban residents lack access to basic
services and infrastructure, such as safe and accessible
drinking water, sanitation, waste collection, all-weather
roads, education, health care, emergency services
and electricity.
Official statistics typically under-estimate the scale
of urban poverty, particularly where they depend on
income-based poverty lines that do not reflect the costs
and realities of living in urban areas. The dollar-a-day
poverty line used in the Millennium Development Goals
is perhaps the most egregious example. Such simplistic
measurements have resulted in a lack of attention to
urban poverty reduction by many governments and
development agencies (Mitlin & Satterthwaite, 2013).
Defining poverty in terms of unmet basic needs, such
as lack of basic services, tenure or safe housing, rather
than in terms of income reveals that the number of
people living in urban poverty has dramatically increased
over recent decades (UN-Habitat , 2016; Satterthwaite,
et al., 2016). Indeed, between 1990 and 2015, many
countries experienced a decline in the proportion of
their urban populations that had with water piped to
premises or improved sanitation (Satterthwaite, 2016).
Yet governments can meet many basic needs at a lower
cost in cities than is typically possible in rural areas.
This is because higher population density reduces unit
distribution costs and permits economies of scale.
In other words, the more people who can connect to
or use a system, the lower the average costs of that
system (Wenban-Smith, 2006; Duranton, 2008; Turok &
McGranahan, 2013). Therefore, although urbanisation is
often associated with poverty, it in fact offers substantial
opportunities to enhance wellbeing. Achieving these
development objectives, and maximising agglomeration
and scale economies, depends on the presence of
enabling policies and infrastructure investments (Turok
& McGranahan, 2013). The persistent scale and depth
of urban poverty therefore represents a chronic failure
of governance.
This failure is in part because governments and
utilities “struggle to generate the resources needed for
providing the trunk infrastructure (for water, waste water,
roads, paths, electricity) to underpin universal provision”
(Satterthwaite & Mitlin, 2014, p. 8). The investment
needs far exceed municipal budgets: local governments
in Bangladesh, Kenya and Nepal, for instance, all have
less than $20 to spend per person per year (UCLG,
2010), most or all of which is needed for recurrent
expenditure such as salaries. Local governments
consequently need to secure large transfers from
national government, attract development assistance or

Cities as e ngin es of eConomiC growth | The case for providing basic infrasTrucTure and services in urban areas
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access capital markets to redress infrastructure deficits.
The challenge of planning, financing and delivering
urban infrastructure is made more complex by the scale
and pace of urbanisation: 1.3 million people move to
urban areas every week (Seto, et al., 2014), with 90 per
cent of this growth taking place in Africa and Asia (UN
DESA, 2014).
The combination of rapid population growth and
underinvestment in infrastructure and services has
led to an increasing number of urban dwellers living
in informal settlements. Standard planning processes
compound this problem, because the regulatory and
legal instruments typically used to implement urban
plans exclude those who cannot participate in formal
land and labour markets (Watson, 2009). Although
the resulting informality can create environmental and
social risks (Dodman, et al., 2016a), it also offers space
for urban residents to develop livelihoods, build or find
homes and access services in the absence of formal
provision. In many cities, a large part of the informal
economy serves formal sector enterprises. Indeed,
the informal sector can be more dynamic and resilient
than the formal sector, for example, in situations where
government regulation inhibits innovation, or where
informal providers of goods and services are able to
move rapidly to serve new areas (Benjamin, et al., 2012;
Brown, et al., 2014). Organised groups of the urban
poor have an impressive record of building community-
level systems, such as piped water systems, community
toilets or sewer and drain networks, to serve informal
settlements (Burra, et al., 2003; Hasan, 2006; Dobson,
et al., 2015; Boonyabancha, et al., 2012). But they
lack the authority, capacity and resources to build
citywide trunk infrastructure, and therefore depend on
partnerships with government (d’Cruz & Mudimu, 2013;
Satterthwaite, 2013).
Yet governments frequently lack the political will to
invest in these parts of the city. This may be because
residents of informal settlements are not seen as
legitimate citizens with rights and entitlements (Bhan,
2009; Patel, et al., 2012); because governments
want to discourage rural-urban migration to levels
consistent with the availability of jobs and services
(Tacoli, et al., 2008; McGranahan, et al., 2013); or
because the concentration of people in urban areas
enables more effective organisation and participation
in political processes (Satterthwaite, 2008; Dodman
& Mitlin, 2013), therefore posing a perceived threat to
governments. These views mean that even governments
that embrace cities as prospective drivers of economic
development can also see large proportions of the
urban population as threats to the functioning of the city,
and therefore allocate resources in ways that perpetuate
poverty and compound exclusion (McGranahan,
et al., 2016).
This paper offers new quantitative research that
demonstrates the low cost of constructing and
operating such urban infrastructure and services,
focusing on the economics of generating and
distributing electricity. There is case study evidence that
sanitation, water, housing and other basic needs can be
met in an affordable way through individual, collective or
public investments (see Chaplin, 2011; Sutherland, et
al., 2014; Banana, et al., 2015), but the economic data,
technological options and delivery models are often not
transferable to other contexts. By comparison, data for
the electricity sector are relatively robust and solutions
can be reproduced across different urban areas. It is
also widely recognised that access to modern energy
is essential for both social and economic development
(Modi, et al., 2006). Access to legal, reliable electricity
in urban areas has been associated with improvements
in household incomes, due to reduced expenditure on
energy and improved productivity; better public health,
due to reduced indoor air pollution and incidence of
burns; and improved security and reduced levels of
domestic violence, due to street and household lighting
(Crousillat, et al., 2010). Ensuring “access to affordable,
reliable, sustainable and modern energy for all” is
accordingly the seventh Sustainable Development Goal
(United Nations Development Programme, 2015).
The paper is structured as follows. Section 2 details
the methods used to calculate the size of the un-served
urban population and the costs of constructing and
operating the necessary infrastructure. The results
are presented in Section 3, both at a global level
and in case studies from South Africa and India. The
implications of this work are considered in Section 4,
which also offers recommendations to policymakers and
practitioners.

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2
Methods
Two data sets were used to estimate the number
of urban dwellers without access to electricity. The
Electricity Access Database in The World Energy
Outlook 2015 (IEA, 2015a) provided the percentage
of the urban population without access to electricity
by country in 2013. These data were multiplied by the
urban population in each country in 2015, projected in
World Urbanization Prospects 2014 (UN DESA, 2014).
The economics of generating and distributing electricity
were calculated assuming that each urban resident
would require 100kWh a year, the definition for ‘modern
energy access’ used by the International Energy Agency
(IEA) (Moss & Gleave, 2014). This level of electricity
access could support two compact fluorescent
light bulbs, two mobile phones and up to three small
appliances (for example, a fan, small refrigerator, radio,
sewing machine, welding appliance or small television
in each household) – enough to significantly enhance
basic quality of life and economic productivity (Yeager,
2001; cited in Pereira, et al., 2010).
The economic needs were calculated from the
perspective of a public utility or government agency,
assuming that additional generation, transmission and
distribution infrastructure would need to be constructed
to meet new demand from previously un-served urban
residents. In practice, there is scope to redistribute the
existing electricity supply in most of these countries to
ensure that all urban residents have sufficient electricity
access. In the short-term, this would provide a just
means to resolve lack of electricity access. In the long-
term, however, all the countries included in this analysis
need to invest in new supply infrastructure to meet
demand. This analysis, therefore, estimates the cost
of meeting the proportion of that demand that would
come from achieving universal electricity access in
urban areas.
Two metrics were used in the economic analysis: the
levelised cost of electricity (LCOE) and the overnight
capital costs in each country or region. These measures
offer different information relevant to prospective
policymakers and planners. LCOE is the cost per
unit of energy ($/MWh) of building and operating a
generating plant over its lifetime. It includes capital,
financing, operating, fuel and maintenance costs.
Calculating the LCOE requires assumptions about
the lifespan, discount rates, interest rates, capacity
factors and utilisation rate of different generation
technologies. The LCOE offers a useful summary of the
competitiveness of different generation technologies (or
a particular package of technologies) over their lifetime.
By comparison, the overnight capital costs reflect the
resources that an investor would need to mobilise if the
power plant were to be constructed overnight. It does
not include the interest incurred during the construction
period, the labour required to construct the plant or the
subsequent costs of operating the plant. The overnight
capital costs are an important indicator of the economic
competitiveness of different generation technologies
because they reflects the fact that many prospective
investors (including governments and utilities) face
limited resource envelopes and significant opportunity
costs, and that their investment options are accordingly
constrained.
The LCOE and upfront investment needs of universal
electricity access were calculated under two scenarios:
1. CONVENTIONAL: Additional demand from
previously un-served urban residents will be met by
electricity generated from sources consistent with
the national or regional average in 2020. In other
words, the LCOE and the upfront investment needs
are calculated based on new capacity projected
to be installed between 2013 and 2020 under
business-as-usual conditions. The conventional

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scenario for each country or region is based on the
New Policies Scenario developed by the IEA, taking
account of broad policy commitments and plans that
have been announced by countries.
2. LOW-CARBON: Additional demand from previously
un-served urban residents will be met with a greater
proportion of electricity generated from low-carbon
technologies. The low-carbon scenario for each
country or region is based on the 450 Scenario
developed by the IEA, which sets out an energy
pathway consistent with limiting the concentration of
greenhouse gases in the atmosphere to 450 parts
per million (which should ensure that the global
average temperature change does not exceed 2°C).
The choice of discount rate is central to calculating
the LCOE, because it determines the rate at which
future costs and benefits are converted into costs
and benefits today. The discount rate is determined
by the opportunity cost of an investment and the
time preference of the investor. Investors using lower
discount rates can be understood to have a longer-
term investment horizon, with greater value placed on
future costs and benefits compared with investors
using higher discount rates, who place a higher value
on costs and benefits in the near-term. Public actors
would typically be expected to use lower discount rates
and to factor wider social returns (such as emission
reductions) into investment decisions than private
actors. We therefore additionally conducted a sensitivity
analysis using four different discount rates (1 per cent,
3 per cent, 5 per cent and 7 per cent). For reference,
The Stern Review on the Economics of Climate Change
used an average discount rate of 1.4 per cent (Stern,
2006), while Nordhaus used an average discount rate
of 5.5 per cent in his critique of The Stern Review
(Nordhaus, 2007). The former can be considered a
social-welfare-equivalent discount rate, while the latter
can be understood as a finance-equivalent discount rate
(Goulder & Williams, 2012).
The analysis assumes that the electricity provided to
the un-served urban population would be part of a
larger bundle of investments in electricity generation
and distribution infrastructure, as specified in the
two IEA scenarios. The calculations below reflect the
proportion of total costs required to provide 100kWh
per person per year to the un-served urban population.
The calculations take into account the additional cost of
electricity due to transmission and distribution losses,
which mean that significantly more electricity has to be
generated than will be consumed. Country-level data on
transmission and distribution losses were obtained from
the World Bank (World Bank, 2014).
The data sources and assumptions for each country/
region are specified in Appendix 1.
Limitations
City-scale data on population and access to services
are inadequate in most low- and middle-income
countries. Dependence on outdated data, collected
according to inappropriate criteria, has long been a
challenge for designing meaningful poverty reduction
strategies in urban areas (Satterthwaite, 2003;
Satterthwaite, 2016). In particular, estimates of the
urban population in many countries are not reliable,
because they are based on projections from censuses
completed 10 or more years ago. Similarly, data on the
share of the population with access to basic levels of
electricity are not robust, due to the variations in the
reliability, legality and affordability of the connection. In
light of these uncertainties, this paper does not project
the future size of the un-served urban population, but
focuses solely on the cost of providing electricity to
those who currently lack access.
This study does not take into account policy frameworks
that may influence the economics of different
technologies. Fossil fuel subsidies, carbon taxes, feed-
in tariffs and other interventions significantly change the
attractiveness of individual generation options. Current
global policy frameworks heavily favour fossil fuels,
with the International Monetary Fund estimating that
post-tax subsidies reached $5.3 trillion of 6.5 per cent
of global GDP in 2015 (Coady, et al., 2015). It is also
worth highlighting that the use of LCOE and overnight
capital cost metrics do not capture technology or price
risks, which may further affect the feasibility of different
policies and investments (Grossa, et al., 2010).
Even in the 450 Scenario (the low-carbon pathway
presented here), the IEA projects that a significant
proportion of new electricity infrastructure will be in
the form of coal and gas power plants, particularly
in low- and lower middle-income countries. There
are compelling arguments to be made against this
approach, particularly with regard to the negative
health costs associated with fossil fuel technologies
(Wilkinson, et al., 2007) and the increased likelihood
of carbon lock-in leading to a global temperature
rise exceeding 2°C (Unruh & Carrillo-Hermosilla,
2006; Bertram, et al., 2015). While recognising these
criticisms, the 450 Scenario was used because it is
technologically viable, unlike some more ambitious
scenarios that depend heavily on technologies which
are not yet commercially or technically feasible, such as
energy storage, carbon capture or tidal/marine energy.
This is particularly important in the context of low- and
lower-middle income countries.

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3
Results
Current levels of electricity
access
Based on data from the International Energy Agency
and United Nations (IEA, 2015a; UN DESA, 2014),
199.2 million urban dwellers currently lack access to a
basic level of electricity. Nearly 140 million of them live in
sub-Saharan Africa (20 per cent in Nigeria alone), with
most of the remainder living in urban centres in South
Asia. The distribution of un-served urban residents is
presented in Figure 1, and the countries with the most
significant un-served populations in Table 1.
If these nearly 200 million urban dwellers were to
each consume 100kWh per year, they would consume
19.9TWh of electricity. For reference, this is roughly
equivalent to the current energy consumption of
Kolkata (population 16.3 million) or 2.5 per cent of the
current energy consumption of Metropolitan New York
(population 22.2 million) (Kennedy, et al., 2015).
Table 1. Countries w ith the largest number a nd proportion of urban residents without access to electricity (IEA , 2015a; UN
DES A, 2 014).
COUNTRIES WITH LARGEST NUMBER
OF UN-SERVED URBAN RESIDENTS
COUNTRIES WITH LARGEST PROPORTION
OF UN-SERVED URBAN RESIDENTS
Nigeria 39,456,225 South Sudan 96%
India 16,797,555 Central African Republic 95%
Democratic Republic of Congo 12,419,095 Sierra Leone 90%
Indonesia 7,970,476 Chad 86%
Myanmar 7,479,950 Liberia 83%
Pakistan 6,854,541 Democratic Republic of Congo 81%
Côte d’Ivoire 6,645,817 Burundi 73%
Angola 5,438,279 Malawi 68%
Bangladesh 5,388,424 Somalia 68%
DPR Korea 5,384,353 Togo 65%

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Figure 1. Global distr ibution of urban dwellers who do not have any access to grid electr icity. Millions more have an un reliable electricity supply or obta in their electricity i llegally.

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Economics of urban
electricity access
The levelised cost of electricity generated from
conventional technologies would be $38.22/MWh.1
When the costs of transmission and distribution
infrastructure are factored in, it would cost $1.37 billion
per year to 2045 to provide a basic level of electricity
for the 200 million urban residents who currently lack
access (Figure 2).
The LCOE is lowest in Latin America, at $32.97/
MWh (Table 2). This is because there is a large share
of hydropower in that part of the world, which is a very
cost-effective option. By comparison, the LCOE in the
Middle East is $44.51/MWh, which is 35 per cent more
than in Latin America.
Under business-as-usual conditions, this new
infrastructure would produce a quantity of greenhouse
gas emissions not compatible with avoiding a 2°C
temperature rise. We therefore evaluated the economics
of pursuing a low-carbon pathway consistent with
limiting the concentration of greenhouse gas emissions
in the atmosphere to 450 parts per million. The LCOE
in this scenario would be $38.82/MWh. Combined
with constructing transmission and distribution
infrastructure, it would cost $1.38 billion per year to
generate electricity from renewable energy sources to
meet the demand of un-served urban populations – in
other words, less than 1 per cent more than in the
conventional scenario.
The economic implications of pursuing a low-carbon
energy pathway vary among regions. In Asia, Latin
America and South Africa, the cost of providing low-
carbon electricity is on average 3.9 per cent higher than
providing electricity under a business-as-usual scenario.
However, the two scenarios are almost equivalent
across the rest of Africa, while the levelised cost of
low-carbon electricity is lower than that of conventional
options in the Middle East.
The composition of electricity in the different scenarios
is shown in Figure 3. This shows that even in the low-
carbon scenario, the IEA still anticipates a substantial
expansion of coal and natural gas power plants
to generate baseload electricity in low- and lower
middle-income countries. In this scenario, renewable
technologies are expected to play a more significant role
in upper middle- and high-income countries that have
the financial and technical capacities necessary to cover
the high upfront costs and to integrate a greater share of
intermittent energy sources into the grid.
Estimates of the LCOE are heavily influenced by
assumptions about the discount rate, or the rate at
which future costs and benefits are converted into
costs and benefits today. The LCOE of electricity
Table 2. The levelised c ost of electricity (USD/MWh) under business-a s-usual conditions (conventional scena rio) and
pursuing an energy pathway consistent with lim iting the concentration of greenhouse gas emissions in the atmosphere to 450
part s per million (low-ca rbon scenario). The LCOE includes the capital, financing , operating, maintenance and fuel costs of
new generation infrastruct ure. These estimates are based on a discount rate of 3 per cent and an interest rate of 5 per cent.
CONVENTIONAL
SCENARIO
LOW-CARBON
SCENARIO
INCREMENTAL
COST OF
LOW-CARBON
SCENARIO
Africa (excluding South Africa) $37.46 $37.66 0.53%
Asia (excluding India) $41.82 $42.38 1.34%
India $36.90 $39.92 8 .18 %
Latin America $32.97 $34.81 5.58%
Middle East $44.51 $42.37 –4.81%
South A frica $38.95 $43.40 11.17%
Weighted average $38.22 $38.82 1.57%
1 The LCOE of electricity is typically expressed in cents per kilowatt-hour. In this paper we have used dollars per megawatt-hour to more clearly show the
difference among regions and scenarios.

Cities as e ngin es of eConomiC growth | The case for providing basic infrasTrucTure and services in urban areas
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Figure 2. Annua l costs (USD) of electricity infrastructure to meet the needs of un-ser ved urban populations, compa ring business-as-us ual and low-carbon options by r egion. These
estimates include the capital, fi nancing, operating, maintena nce and fuel costs of the generation, transmission and distribution in frastruct ure.

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is also influenced by the interest rate, because this
determines the cost of borrowing money to finance new
infrastructure investment. The higher the proportion of
upfront costs, the more significant the impact of the
discount rate and interest rate will be on the LCOE.
The LCOE above (Table 2, Figure 2) is based on a
discount rate of 3 per cent and interest rate of 5 per
cent. Figure 4 shows the global LCOE under a range
of different discount and interest rates. This sensitivity
analysis demonstrates that conventional generation
options prove increasingly attractive with higher
discount rates. This reflects the different cost profiles of
the two scenarios. Conventional options typically have
low overnight capital costs compared with low-carbon
technologies, but require significant expenditure on
fuel throughout their lifetime: for instance, fuel costs
represent 35 per cent of the LCOE for coal-fired power
plants, and 73 per cent for gas-fired power plants
Figure 3. Electricit y generation (%) by fuel ty pe in the conventional scena rio (above) and low-carbon scenar io (below).
Hydropower
Coal
Gas
Oil
Nuclear
Wind
Solar photovoltaics
Bioenergy
Concentrated solar power
Geothermal
Hydropower
Coal
Gas
Oil
Nuclear
Wind
Solar photovoltaics
Bioenergy
Concentrated solar power
Geothermal

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(IEA, 2015d). By comparison, the greater capital costs
of renewable energy systems mean that prospective
investors have to borrow more and therefore face higher
financing costs. This means that a higher interest rate
increases the cost of low-carbon technologies more
than conventional options. Therefore, the transition to
a low-carbon energy pathway would be substantially
enabled by low interest rates and large-scale investment
by actors with low discount rates, such as government
agencies. Under the most favourable conditions
(discount rate of 1 per cent and interest rate of 2.5 per
cent), the LCOE of low-carbon electricity is 0.3 per
cent cheaper than that generated from conventional
technologies.
The upfront costs are also an important consideration
for prospective investors (whether public or private).
New infrastructure in the low-carbon scenario has
higher investment needs than that in the conventional
scenario, because renewable energy technologies
are capital-intensive but do not incur fuel costs, so
expenditure is ‘front-loaded’. By comparison, coal- and
gas-fired power plants tend to have lower investment
needs than renewables, but the LCOE is comparable
due to the ongoing cost of fuel inputs.
Under business-as-usual conditions, it would cost $8.5
billion to build enough new infrastructure to generate
electricity for the 200 million un-served urban residents.
It would cost an additional $509.8 million if this new
generation capacity was consistent with maintaining
global carbon dioxide levels at or below 450ppm, 6 per
cent more than it would cost using conventional options.
However, as the LCOE analysis above indicates, much
of this would be recovered from the lower operating
costs associated with renewable energy technologies.
The additional capital costs of the low-carbon scenario
vary greatly by region. In Asia (excluding India), the
capital costs of low-carbon options are less than those
of conventional options. By comparison, the low-carbon
investment needs are substantially higher than those
Figure 4. Sensitivity analysis: the weighted average of the levelised c ost of electricity (USD/MWh) under a range of di erent
discount rates and interest rates. The LCOE includes the capita l, operating, maintenance and fina ncing costs of generation
infrastructure.
$60
$50
$40
$30
$20
$10
$0
Levelised cost of electricity (USD/MWh)
2.5% 5.0% 7.5% 2.5% 5.0% 7.5% 2.5% 5.0% 7.5% 2.5% 5.0% 7.5%
1% 3% 5% 7%
■ Conventional scenario ■ Low-carbon scenario
Interest Rate
Discount Rate

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required in the conventional scenario in Latin America
and South Africa (Table 3 and Figure 5).
This analysis clearly shows that the levels of investment
needed to provide electricity to un-served populations
are not significant. When the operating (including
fuel), maintenance and financing costs are added to
the overnight capital costs, governments would need
to mobilise $761.2 million per year to 2045, or $773.1
million in the low-carbon scenario. Transmission and
distribution would require a further $607.7 million per
year in both scenarios.
With 135.9 million un-served urban residents in
Africa, it is unsurprising that most of this investment
(67.3 per cent) would be required in this region, with
nearly 40 per cent of this in Nigeria and the Democratic
Republic of the Congo alone. Critically, the costs of
pursuing a low-carbon scenario on the African continent
are not significantly different over the lifetime of the
infrastructure, although the overnight capital costs are
6.9 per cent higher. Most of the remaining investment
would be required in Asia, overwhelmingly South Asia.
Although mobilising resources at this scale is a
challenge for municipal authorities, utilities and even
national governments, this is not a significant sum in the
context of current flows of development assistance and
infrastructure investment. For instance, under the Paris
Agreement2, developed countries have committed to
mobilise $100 billion annually by 2020 for mitigation and
adaptation (UNFCCC, 2015). Moreover, prospective
investors could expect to recover most of their costs
through electricity bills: in both scenarios the annual
costs equate to less than $7 per person per year, which
most urban residents would be able to pay.
Two case studies are presented below, which serve
to illustrate both the opportunities and challenges
facing cities.
Case study: South Africa
South Africa’s deep inequalities manifest in its uneven
patterns of energy access and use. High-income
households and industry consume substantial amounts
of electricity, which contributes to the country’s high
per capita emissions of 9.3 tonnes of carbon dioxide
equivalent (World Bank, 2016b). Yet 3.5 million South
African urban dwellers do not have access to electricity.
This is a legacy of the apartheid era: just 36 per cent of
South African households had access to electricity in
1994 (Pegels, 2010).
The generation infrastructure necessary to provide
100kWh of electricity annually to all un-served urban
residents would cost $16.9 million per year to 2045
(including capital, financing, operating, maintenance
and fuel costs). The low LCOE ($38.95/MWh) is
mostly due to the substantial and easily accessible coal
reserves in the country (Pegels, 2010). By comparison,
generating this electricity from low-carbon technologies
would cost $18.2 million per year, 8 per cent more than
that generated under business-as-usual conditions,
even though coal and gas would still make a significant
contribution to electricity generation (Figure 6). Over
the same period, South Africa would need to invest a
further $11.6 million each year to finance connection
and distribution infrastructure.
Table 3. Overnig ht capital costs of the elect ricity generation infra structure required to provide 100kW h per year to currently
un-served urban residents, under busi ness-as-usual and low-carbon scenarios.
CONVENTIONAL
SCENARIO
LOW-CARBON
SCENARIO
INCREMENTAL
COST OF
LOW-CARBON
SCENARIO
Africa (excluding South Africa) $5,695,010,919 $6,086,046,274 6.9%
Asia (excluding India) $1,500,584,274 $1,488,987,375 –0.8%
India $689,324,227 $740,259,134 7.4%
Latin America $404,310,783 $4 51,50 0,970 11.7 %
Middle East $78,968,156 $86,451,300 9.5%
South A frica $132,372,600 $157,132,797 18.7%
TOTAL $8,500,570,958 $9,010,377,851 6.0%
2 The Paris Agreement is an agreement within the United Nations Framework Convention on Climate Change (UNFCCC), negotiated at the 21st Conference of
Parties of the UNFCCC in December 2015. The agreement sets out a global action plan to avoid dangerous levels of climate change by limiting global warming
to well below 2°C above pre-industrial levels, and to seek to limit the temperature increase to 1.5°C.

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16 www.iied.org
Figure 5. Overnight capital costs ( US$2012) of cons tructing electr icity supply infrast ructure to meet the nee ds of un-served urban populations, comparing business-as-u sual and low-
carbon options by region.

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Despite the higher upfront costs, the South African
government is unlocking significant private finance
for renewable energy technologies: nearly 780MW of
solar photovoltaics and 560MW of wind power were
added in 2014 alone. The government has facilitated
this investment through enabling policies such as net
metering, utility quota obligations, auctions, capital
subsidies or rebates and tax exemptions (IRENA, 2015).
Case study: India
Nearly 17 million Indian urbanites do not have access
to electricity. Nearly a third of these people live in the
urban centres of Uttar Pradesh, although there are also
more than two million urban residents without access to
electricity in Bihar and West Bengal (IEA, 2015b).
The cost of providing these people with a basic level
of electricity is equal to $94.1 million per year to 2045,
including capital, operating, maintenance and financing
costs. This equates to a LCOE of $36.90/MWh.
Electricity generated in the low-carbon scenario would
have a levelised cost of $39.92/MWh, which means
that India would need to spend $101.9 million per year
to provide electricity to the un-served urban population.
This is 8.2 per cent higher than electricity generated
under business-as-usual conditions, even though low-
cost coal is still a significant source of energy in the low-
carbon scenario (Figure 7). India’s coal has low calorie
and high ash content (Garg, 2012), and the resulting air
pollution has the most severe impacts on low-income
and other marginalised groups (Foster & Kumar, 2011;
Garg, 2011). These health considerations create an
additional incentive for decision-makers to choose clean
energy technologies.
Although most urban residents in India have access
to electricity, many depend on illegal connections to
the grid. This is often because electricity utilities do
not have policies and practices in place to work with
informal settlements: for instance, residents may lack
the documented proof of address needed to register
for new connections to the grid or live on land owned
by national agencies that prevent any infrastructure or
service provision. Community-based organisations and
non-government organisations are playing an important
role in engaging with utilities to adopt more flexible and
inclusive systems. The Mahila Housing SEWA Trust
in Ahmedabad, for example, has worked with local
utilities to change their policies, so that residents can
legally connect to the grid under a new category of
electricity bills that cannot be used as proof of address,
Figure 6. Levels and composition of electricity supply in South A frica in 2020 in the conventional and low-carbon scenarios
(IEA , 2014c). The low-ca rbon scenario is heavi ly dependent on energ y eciency measures, so requires less electr icity to be
generated.
300
290
280
270
260
250
240
230
220
Electricity supply (TWh)
■ Geothermal
■ CSP
■ Bioenergy
■ Solar PV
■ Wind
■ Hydropower
■ Nuclear
■ Oil
■ Gas
■ Coal
Conventional Low-carbon

Cities as e ngin es of eConomiC growth | The case for providing basic infrasTrucTure and services in urban areas
18 www.iied.org
therefore creating space for informality within formal
systems. Such initiatives demonstrate the important
role that organised communities can play in securing
basic infrastructure and services through advocacy to,
and partnership with, government. This example also
shows that, although the analysis above suggests that
India needs to invest $56 million per year on distribution
infrastructure to connect un-served households, most
cities would benefit from additional policy interventions
and investment in the grid to ensure that informal
settlements have a legal, reliable electricity supply.
Figure 7. Levels a nd composition of electricity supply in India in 2020 in the conventiona l and low-carbon scenar ios (IEA,
2014c). The low-carbon scenar io is heavily dependent on energy eciency measu res, so requires less elect ricity to be
generated.
2000
1800
1600
1400
1200
1000
800
600
400
200
0
Electricity supply (TWh)
■ Marine
■ Geothermal
■ CSP
■ Bioenergy
■ Solar PV
■ Wind
■ Hydropower
■ Nuclear
■ Oil
■ Gas
■ Coal
Conventional Low-carbon

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4
Discussion
This analysis demonstrates that generating and
distributing electricity to un-served urban populations
does not require significant resources: using
conventional generation technologies, only $1.37 billion
per year would be needed to generate and distribute
100kWh to each urban resident who currently lacks
access. This equates to less than 0.03 per cent of
annual fossil fuel subsidies (Coady, et al., 2015). Two-
thirds of these resources would be required in Africa,
where most un-served urban residents reside. With
rapid urban population growth across the continent
and continuing underinvestment in infrastructure, the
investment needs are likely to increase significantly over
coming decades.
Such an investment could have a multiplier effect
on health (for example, by reducing the incidence of
burns or accidental fires), education (for example, by
generating light to study) and telecommunications (for
example, providing the power to charge mobile phones).
Access to electricity also facilitates income-generating
activities, such as fabrication of textile goods and food
processing (Modi, et al., 2006; Chen, 2016).
Investment in other basic services and infrastructure are
likely to have similarly low per capita costs, and would
offer different socio-economic benefits. Provision of
affordable, reliable and accessible drinking water, for
instance, reduces the incidence of disease for urban
residents, thereby reducing costs, reducing income lost
to being off work and enhancing productivity (Mitlin &
Walnycki, 2016). Tenure can increase the security of
low-income groups and thereby facilitate investment
in housing and land (Budds, et al., 2005; Payne,
et al., 2009).
Yet planning and investment that benefits the poor is
only likely to take place in cities where the voices of
low-income and other marginalised groups are heard
and organised (Watson, 2009). Too often, formal policy
frameworks and markets exclude these groups. For
instance, in many countries, the scale of electricity
theft is viewed as a major problem because it means
that utilities cannot finance future investments in
new generating capacity – yet residents of informal
settlements are not permitted to legally connect to
the grid to pay for the electricity they consume (Smith,
2004; Depuru, et al., 2011). Urban planning and
governance systems need to find ways to bridge the
gap between formal and informal systems if they are to
deliver basic infrastructure and services to un-served
urban residents.
This research also shows how cities can pursue
climate-compatible development without having to
unlock significant additional investment. In the case of
the electricity sector, the cost of providing electricity
from low-carbon rather than conventional generation
options would be less than 1 per cent higher over the
lifetime of the technologies. This is consistent with
other literature on the economics of low-carbon cities,
which reveal large opportunities to reduce emissions
through cost-effective investments in cities in low- and
middle-income countries (Colenbrander, et al., 2015;
Colenbrander, et al., forthcoming). With favourable
interest rates or rapid technological learning, renewable
energy technologies could prove even more competitive
(Sudmant, et al., 2016).

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While the low-carbon scenario is only marginally more
expensive than the conventional scenario in the long-
term, the difference in the near-term is significant: the
overnight capital costs are 6 per cent higher. In the
context of widespread urban poverty, decision-makers
must consider the opportunity costs of this incremental
investment. There is an equally compelling economic
and social case to be made that these resources
should be spent on other infrastructure or services,
rather than on lower-carbon electricity infrastructure
(Colenbrander, et al., forthcoming). Urban populations’
current vulnerability to climate change is largely due to
deficits and deficiencies of basic infrastructure, such
as drainage and sanitation (Satterthwaite, et al., 2009).
There is therefore a clear complementarity between
poverty reduction and adaptation in urban areas (Ayers
& Dodman, 2010). To further enhance the adaptive
capacity of low-income urban residents, it is important
that governments and other decision-makers create
opportunities for meaningful community participation in
planning and delivering this infrastructure. This helps to
integrate the informal sector into formal urban planning
and governance systems, and to challenge the power
relations that perpetuate and compound poverty and
vulnerability (Archer, et al., 2014; Dodman, et al., 2016b).
It is essential not to downplay the challenges
of financing these kinds of public goods. Trunk
infrastructure is expensive and the costs are front-
loaded, while the returns are often uncertain and diffuse.
However, there is a clear revenue stream from these
investments via electricity bills. Per capita costs are
equivalent to less than $7 a year, which even low-
income urban residents would almost always be able to
pay. The challenge is in meeting the high upfront costs,
particularly in the low-carbon scenario.
Discrete, technically straightforward infrastructure
projects of this nature should be able to secure
private finance, with the possible exception of those
in fragile and conflict-affected states where the risks
are higher. The scale of the opportunity should be
particularly attractive to institutional investors such as
pension funds, sovereign wealth funds and insurance
companies, which have substantial financial resources
and long investment horizons. But public finance needs
to play an anchoring role in improving risk-adjusted
returns to private investors. National governments
have an important role to play in establishing enabling
policy frameworks. To illustrate, Indonesia has immense
geothermal resources, but is struggling to attract private
investment because its tender processes currently
favour coal-fired power plants (Smith, 2012). National
governments can also offer incentives, such as tax
rebates, tax exemptions, capital grants/subsidies and
feed-in tariffs, which help infrastructure projects to
attract private finance by improving the rate of return.
South Africa has successfully deployed many of these
policies, and is seeing a commensurate increase in
wind and solar generation capacity (IRENA, 2015).
Electricity utilities can reduce the risk to prospective
investors by offering power purchase agreements, while
development banks and climate funds play a similar
role by offering credit guarantees, first-loss capital or
insurance to prospective infrastructure investments.
Finally, municipal authorities can work with community-
based organisations to map informal settlements and
ensure that the new infrastructure serves all residents.
In sectors such as water, sanitation and solid waste
collection, municipal authorities and community-based
organisations can play a larger role in constructing and
financing new infrastructure.
In an ideal world, poverty reduction would be incentive
enough to build the political will and strengthen
the institutional capacities required to make these
investments. In the absence of such commitment,
it is necessary to highlight the economic case for
the universal provision of basic infrastructure and
services. This case would be strengthened from greater
quantitative analysis of the impacts, such as the value
of public health improvements in informal settlements or
productivity gains in the informal economy.
A growing body of evidence suggests that extreme
inequality constrains economic growth. Recent research
from the International Monetary Fund shows that
increasing the income share of the poor leads to higher
GDP growth, while increasing the income share of the
rich is associated with a decline in GDP growth (Dabla-
Norris, et al., 2015). This is because inequality limits
the scope for low-income groups to accumulate human
and physical capital, thereby reducing net productivity
(Stiglitz, 2012). Income stagnation for the poor can also
fuel political crises (through the breakdown of social
cohesion) and financial crises (through the adoption of
populist policies) (Milanovic, 2016).

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As governments come together at Habitat III to agree
on a New Urban Agenda3, it is imperative that they
recognise that sustained economic growth is likely to
depend on reducing inequality within and among cities.
In particular, national and local governments need
to prioritise poverty reduction through the provision
of basic infrastructure and services, such as safe
drinking water piped to homes, improved sanitation,
household waste collection, durable housing, health
care, education and electricity. By improving livelihoods,
public health and resilience to shocks and stressors,
these measures increase the capacity of low-income
and other marginalised groups to contribute to the city
economy. Such measures are also likely to enhance
political stability and social capital by reducing poverty
and facilitating wider participation in urban economies
and societies. Although the costs of constructing
and operating these systems may seem significant
compared to municipal budgets, decision makers need
to recognise that they are essential investments if cities
are to achieve their potential as engines of economic
growth. As Barbara Ward (1976) wrote for Habitat I:
“Cities must not be built for economics alone –
to build up the property market – not for politics
alone – to glorify the Prince (in whatever form
of government). They must be built for people
and for the poorest first.”
3 Habitat III is shorthand for the United Nations Conference on Housing and Sustainable Development, which is held every twenty years. The purpose is to renew
political commitment to sustainable urban development and identif y global priorities and pathways to deliver it in the New Urban Agenda. A draft of the New
Urban Agenda was agreed in Surabaya in S eptember 2016, and will be adopted in Quito in October 2016.

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Appendix 1: Data
sources and
assumptions
The World Energy Investment Outlook 2014 (IEA,
2014b) provided the data on different power generation
technologies, including capital costs, operating and
maintenance costs, efficiency, capacity factors,
construction times and learning rates. We use the
figures projected for 2020 in this analysis.
The net present value (NPV), integral to calculating the
LCOE, is evaluated for each technology and region over
a 30-year period, including construction times obtained
from the IEA (IEA, 2014b). As illustrated above, the
LCOE of each bundle of technologies was tested using
a range of different discount rates and financing costs.
In all scenarios, photovoltaic solar panels, wind turbines
and concentrated solar power have average lifespans of
25 years, while nuclear, geothermal and some fossil fuel
plants may last twice as long. Including returns beyond
this horizon may drive down estimates of the LCOE for
conventional technologies. However, this would have
only a small impact due to the front-ended nature of the
costs and the impact of discounting – and in practice,
few investors evaluate returns over such a long period.
We assume that new oil-based generation capacity
using oil is in the form of combined-cycle gas turbines.
This is in part due to lack of data on the capital costs
of oil-fired power plants, and the improbability of
construction of dedicated oil-fired power plants in the
future. Data on global grid-connected biomass capacity
by feedstock and country/region were obtained from
the International Renewable Energy Agency (IRENA,
2012, p. 21). We assumed transmission and distribution
costs of $500 per household (World Bank, 2012),
with average household sizes calculated using data
from the World Economic Factbook (Euromonitor
International, 2013).
Some energy prices were drawn from the World Bank
Commodities Price Forecast: coal produced in Australia
is projected to cost US$54.8/mt in 2020, crude oil to
cost US$65.6/bbl and natural gas produced in Europe
to cost US$5.8/mmbtu (World Bank, 2016a). We
use the current, long-term cost of uranium: $1,880/
kg (including the costs of processing, enrichment and
fabrication) (World Nuclear Association, 2016).
Africa (excluding
South Africa)
The African analysis covers the following countries with
un-served urban populations: Angola, Benin, Botswana,
Burkina Faso, Burundi, Cameroon, Central African
Republic, Chad, Comoros, Congo, Côte d’Ivoire,
Democratic Republic of Congo, Djibouti, Equatorial
Guinea, Eritrea, Ethiopia, Gabon, Gambia, Ghana,
Guinea, Guinea-Bissau, Kenya, Lesotho, Liberia,
Madagascar, Malawi, Mali, Mauritania, Mozambique,
Namibia, Niger, Nigeria, Réunion, Rwanda, Sao Tome
and Principe, Senegal, Sierra Leone, Somalia, South
Africa, South Sudan, Sudan, Swaziland, Tanzania, Togo,
Uganda, Zambia and Zimbabwe.
The conventional scenario in Africa is based on the New
Policies Scenario developed by the IEA (IEA, 2014a,
p. 192), while the low-carbon scenario is based on its
450 Scenario (IEA, 2014c, p. 669). We subsequently
subtracted data for South Africa, which we considered
independently (IEA, 2014c, pp. 676, 677). It is important
to note that the low-carbon scenario put forward for
Africa by the IEA has nearly the same level of renewable
energy capacity as the conventional scenario. The
reduced carbon intensity of electricity is primarily due
to lower levels of investment in fossil fuel power plants
(particularly coal and gas) and assumptions about
improved energy efficiency (see Table 4). This might
reduce continent-wide expenditure on electricity supply
infrastructure, but does not significantly change total
investment needs to address the unmet demand of
urban residents. In other words, the economic case for
the conventional and low-carbon scenarios are very
similar in this region.

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We assume that new wind-based generation capacity
in Africa is from offshore turbines. We assume that
all coal-fired power plants in the region are sub-
critical rather than supercritical. We assume that all
hydroelectricity serving urban grids is generated from
large hydropower plants, as these generate the vast
proportion of hydroelectricity and the small hydropower
plants in the region are a major source of energy for
micro- or off-grid systems in rural areas (IEA, 2014a).
We assume transmission and distribution losses of
17.9 per cent. This figure is calculated using a weighted
average according to the number of un-served urban
residents in each country, with countries in sub-
Saharan Africa (excluding South Africa) have average
transmission and distribution losses of 18 per cent and
those in North Africa of 14 per cent (IEA, 2014a).
Asia (excluding India)
The Asian analysis covers the following countries with
un-served urban populations: Bangladesh, Cambodia,
Indonesia, the Democratic People’s Republic of Korea,
Laos, Malaysia, Mongolia, Myanmar, Nepal, Pakistan,
the Philippines, Sri Lanka, Thailand and Vietnam.
The conventional scenario is based on the New Policies
Scenario developed for non-OECD Asia by the IEA
(IEA, 2015a, p. 630), while the low-carbon scenario
is based on its 450 Scenario (IEA, 2014c, p. 653).
We subsequently subtracted data for India, which we
considered independently, and China, which has no un-
served urban population and has a large enough grid to
distort the findings. The conventional scenarios for India
and China are based on the New Policies Scenario
developed for the countries by the IEA (IEA, 2015b, pp.
634, 636), while the low-carbon scenario is based on
its 450 Scenario (IEA, 2014c, pp. 657, 661).
Capital, operating and maintenance costs for different
energy technologies are assumed to be equal to those
of India, provided in the World Energy Investment
Outlook 2014 (IEA, 2014b). We assume that 10 per
cent of new hydropower capacity is from small hydro,
and 90 per cent from large hydro, which is consistent
with projected global averages to 2040 (IEA, 2015a, p.
353). We assume transmission and distribution losses
of 15.5 per cent, an average generated from country-
level data (World Bank, 2014) and weighted according
to the number of un-served urban residents in each
country. The high rate of transmission and distribution
losses is largely due to Myanmar and Pakistan, which
have 2 million and 1.2 million un-served urban residents
respectively, as well as transmission and distribution
losses of 27 per cent and 13 per cent.
Table 4. Key data points use d to estimate investment needs a nd levelised cost of electricit y in Africa (IEA, 2014a; IE A, 2014c).
NEW POWER GENERATION
CAPACITY (2013–2020) (GW)
COMPOSITION OF
ELECTRICITY SUPPLY IN 2020
(TWH)
BUSINESS-
AS-USUAL
SCENARIO
LOW-CARBON
SCENARIO
BUSINESS-
AS-USUAL
SCENARIO
LOW-CARBON
SCENARIO
Coal 26 17 303 282
Gas 117 77 383 343
Oil 4 1 93 90
Nuclear 0 0 13 13
Hydropower 68 74 182 188
Wind 7 8 14 15
Solar PV 6 6 11 11
Bioenergy 5 5 11 11
CSP 3 3 5 5
Geothermal 7 7 9 9
Marine 0 0 0 0

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India
The conventional scenario in India is based on the New
Policies Scenario developed by the IEA (IEA, 2015b, p.
176), while the low-carbon scenario is based on its 450
Scenario (IEA, 2014c, p. 661).
We assume transmission and distribution losses of 17.7
per cent in 2020 (IEA, 2014c), and that new wind-
based generation capacity in India is from onshore
turbines. We use a price of US$13/tonne for bagasse
and US$26/tonne for rice husks (IRENA, 2012, p. 31),
and assume that utilities can use biogas at no extra cost.
Table 5. Key data points used to estimate investment needs and levelised c ost of electricity in non-OECD A sia, excluding China
and India ( IEA, 2015a; IE A, 2014c).
NEW POWER GENERATION
CAPACITY (2013–2020) (GW)
COMPOSITION OF ELECTRICITY
SUPPLY IN 2020 (TWH)
BUSINESS-
AS- USUAL
SCENARIO
LOW-CARBON
SCENARIO
BUSINESS-
AS- USUAL
SCENARIO
LOW-CARBON
SCENARIO
Coal 49 34 652 540
Gas 28 23 557 550
Oil 0 0 83 86
Nuclear 3 3 72 72
Hydropower 23 19 238 232
Wind 4 4 10 12
Solar PV 8 5 13 10
Bioenergy 4 3 32 36
CSP 0 0 2 0
Geothermal 2 2 29 30
Marine 0 0 0 0
Table 6. Key data points used to estimate investment needs and levelised c ost of electricity in Indi a (IEA, 2015b; IEA , 2014c).
NEW POWER GENERATION
CAPACITY (2013–2020) (GW)
COMPOSITION OF ELECTRICITY
SUPPLY IN 2020 (TWH)
BUSINESS-
AS-USUAL
SCENARIO
LOW-CARBON
SCENARIO
BUSINESS-
AS-USUAL
SCENARIO
LOW-CARBON
SCENARIO
Coal 90 61 1,224 1,072
Gas 22 30 96 154
Oil 1 2 26 18
Nuclear 5 4 66 64
Hydropower 16 15 174 174
Wind 21 22 93 77
Solar PV 15 13 40 22
Bioenergy 4 3 48 46
CSP 1 1 0 2
Geothermal 0 0 0 0
Marine 0 0 0 0

IIED WorkIng papEr
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Latin America
The Latin American analysis covers the following
countries with un-served urban populations: Argentina,
Bolivia, Colombia, Costa Rica, Cuba, Dominican
Republic, Ecuador, El Salvador, Guatemala, Haiti,
Honduras, Jamaica, Panama, Paraguay, Peru, Trinidad
and Tobago, Uruguay and Venezuela.
The conventional scenario is based on the New Policies
Scenario developed for Latin America by the IEA
(IEA, 2015a, p. 654), while the low-carbon scenario is
based on its 450 Scenario (IEA, 2014c, p. 681). We
subsequently subtracted Brazil’s installed capacity
(GW) and electricity supply (TW) because the size of
its grid distorted the regional results, and there are no
un-served urban populations in Brazil. The conventional
scenario for Brazil is based on the New Policies
Scenario developed for the country by the IEA, while the
low-carbon scenario is based on its 450 Scenario (IEA,
2014c, p. 685).
We assume that all existing coal-fired power plants in
Latin America are subcritical, and that coal-fired power
plants constructed from 2016 will be supercritical.
Although there is substantial potential for offshore
wind around Chile, Colombia and Peru, this requires
substantial technical capacities so we assume all
new wind infrastructure will be from onshore turbines.
Capital, operating and maintenance costs for different
energy technologies are assumed to equal those of
Brazil, provided in the World Energy Investment Outlook
2014 (IEA, 2014b). We use a price of US$12/tonne
for bagasse and US$71/tonne for charcoal, based on
prices in Brazil (IRENA, 2012, p. 31), and assume that
utilities can use sewage gas at no extra cost.
We assume transmission and distribution losses of 37.9
per cent, an average generated from country-level data
(World Bank, 2014) and weighted based on the number
of un-served urban residents in each. The high rate of
transmission and distribution losses is largely due to
Haiti, which has 3.45 million unserved urban residents,
accounting for 74.2 per cent of the un-served urban
population in Latin America, and has transmission and
distribution losses of 54 per cent.
Table 7. Key dat a points used to estimate i nvestment needs and levelised cost of electricity in Latin America , excluding Brazil
(IEA, 2015a; IEA, 2014c).
NEW POWER GENERATION
CAPACITY (2013–2020) (GW)
COMPOSITION OF ELECTRICITY
SUPPLY IN 2020 (TWH)
BUSINESS-
AS-USUAL
SCENARIO
LOW-CARBON
SCENARIO
BUSINESS-
AS-USUAL
SCENARIO
LOW-CARBON
SCENARIO
Coal 2 1 21 17
Gas 13 7167 168
Oil 0 0 122 96
Nuclear 1 1 9 14
Hydropower 14 17 359 377
Wind 2 3 7 6
Solar PV 2 1 3 2
Bioenergy 1 3 14 14
CSP 0 0 0 0
Geothermal 0 1 5 5
Marine 0 0 0 0

Cities as e ngin es of eConomiC growth | The case for providing basic infrasTrucTure and services in urban areas
26 www.iied.org
Middle East
The Middle East analysis covers the following countries
with un-served urban populations: Iraq, Oman, Qatar,
Saudi Arabia and Yemen.
The conventional scenario for the Middle East is based
on the New Policies Scenario developed by the IEA
(IEA, 2015a, p. 640), while the low-carbon scenario is
based on its 450 Scenario (IEA, 2014c, p. 665).
We assume that the 1GW of coal-fired power capacity
to be constructed between 2013 and 2020 will be
supercritical and that new wind power capacity comes
from on-shore sources. We find that more than 99 per
cent of new hydropower is in the form of ‘large’ plants,
i.e. 10MW or more. We assume transmission and
distribution losses of 9.8 per cent, an average generated
from country-level data (World Bank, 2014) and
weighted according to the number of un-served urban
residents in each of the five countries.
South Africa
The conventional scenario for South Africa is based on
the New Policies Scenario developed by the IEA, while
the low-carbon scenario is based on its 450 Scenario
(IEA, 2014c, pp. 676, 677).
We assume that new wind-based generation capacity
in Africa is from offshore turbines. We assume that the
only supercritical coal-fired power plants in the country
are the Kusile and Medupi plants, with a combined size
of 4,800MW. We assume that all hydroelectricity that
serves urban grids is generated from large hydropower
plants, because these generate the vast proportion
of hydroelectricity, and the small hydropower plants in
the region are a major source of energy for micro- or
off-grid systems in rural areas (IEA, 2014a). We assume
transmission and distribution losses of 10 per cent
(IEA, 2014a).
Table 8. Key data points used to estimate investment needs and levelised cost of electricity i n the Middle East (IE A, 2015a;
IEA, 2014c).
NEW POWER GENERATION
CAPACITY (2013–2020) (GW)
COMPOSITION OF ELECTRICITY
SUPPLY IN 2020 (TWH)
BUSINESS-
AS-USUAL
SCENARIO
LOW-CARBON
SCENARIO
BUSINESS-
AS-USUAL
SCENARIO
LOW-CARBON
SCENARIO
Coal 1133
Gas 68 50 826 725
Oil 16 6 330 291
Nuclear 4 2 37 20
Hydropower 4 2 30 28
Wind 1224
Solar PV 3345
Bioenergy 0023
CSP 1113
Geothermal 0000
Marine 0000

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www.iied.org 27
Table 9. Key data points used to est imate investment needs and leveli sed cost of electricity in S out h Africa (IE A, 2014c).
NEW POWER GENERATION
CAPACITY (2013–2020) (GW)
COMPOSITION OF ELECTRICITY
SUPPLY IN 2020 (TWH)
BUSINESS-
AS-USUAL
SCENARIO
LOW-CARBON
SCENARIO
BUSINESS-
AS-USUAL
SCENARIO
LOW-CARBON
SCENARIO
Coal 9 6 257 245
Gas 3 2 4 4
Oil 0 0 0 0
Nuclear 0 0 13 13
Hydropower 1 1 4 4
Wind 2 2 5 5
Solar PV 3 3 5 5
Bioenergy 1 1 4 4
CSP 1 1 2 2
Geothermal 0 0 0 0
Marine 0 0 0 0

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28 www.iied.org
References
Archer, D., Alamansi, F., DiGregorio, M., Roberts, D.,
Sharma, D., Syam, D. 2014. Moving towards inclusive
urban adaptation: approaches to integrating community-
based adaptation to climate change at city and national
scale. Climate and Development, 6(4), pp. 345–356.
Ayers, J. & Dodman, D., 2010. Climate change
adaptation and development I: the state of the debate.
Progress in Development Studies, 10(2), pp. 161–168.
Banana, E. et al., 2015. Sharing reflections on inclusive
sanitation. Environment and Urbanization, 27(1), pp .
19–34.
Benjamin, N. et al., 2012. The Informal Sector in
Francophone Africa Firm Size, Productivity, and
Institutions. Washington DC, USA: World Bank.
Bertram, C. et al., 2015. Carbon lock-in through capital
stock inertia associated with weak near-term climate
policies. Technological Forecasting and Social Change,
90(A), pp. 62–72.
Bhan, G., 2009. “This is no longer the city I once knew”.
Evictions, the urban poor and the right to the city in
millennial Delhi. Environment and Urbanization, 21(1) ,
pp. 127–142.
Boonyabancha, S., Carcellar, F. & Kerr, T., 2012. How
poor communities are paving their own pathways to
freedom. Environment and Urbanization, 24(2), pp.
441–4 62.
Brown, D., McGranahan, G. & Dodman, D., 2014. Urban
informality and building a more inclusive, resilient and
green economy. London, UK: International Institute for
Environment and Development.
Budds, J., Teixeira, P. & SEHAB, 2005. Ensuring the
right to the city: pro-poor housing, urban development
and tenure legalization in São Paulo, Brazil. Environment
and Urbanization, 17(1), p. 8 9 –114.
Burra, S., Patel, S. & Kerr, T., 2003. Community-
designed, built and managed toilet blocks in Indian
cities. Environment and Urbanization, 15(2), pp. 11–32.
Chaplin, S., 2011. Indian cities, sanitation and the state:
the politics of the failure to provide. Environment and
Urbanization, 23(1), pp. 57–70.
Chen, M., 2016. Technology, informal workers and
cities: insights from Ahmedabad (India), Durban (South
Africa) and Lima (Peru). Environment and Urbanization.
Coady, D., Parry, I., Sears, L. & Shang, B., 2015. How
Large Are Global Energy Subsidies? Paris, France:
International Monetary Foundation.
Colenbrander, S., Gouldson, A., Roy, J., Kerr, N., Sarkar,
S., Hall, S., Sudmant, A., Ghatak, A., Chakravarty, D.,
Ganguly, D., McAnulla, F. Forthcoming. Can low-carbon
urban development be pro-poor? Environment and
Urbanization.
Colenbrander, S., Gouldson, A., Sudmant, A. &
Papargryopoulou, E., 2015. The economic case for
low-carbon development in rapidly growing developing
world cities: A case study of Palembang, Indonesia.
Energy Policy, Volume 80, pp. 24–35.
Crousillat, E., Hamilton, R. & Antmann, P., 2010.
Addressing the Electricity Access Gap. Washington
DC, USA: World Bank.
Dabla-Norris, E. et al., 2015. Causes and
Consequences of Income Inequality: A Global
Perspective. Washington DC, USA: International
Monetary Foundation.
d’Cruz, C. & Mudimu, P., 2013. Community savings
that mobilize federations, build women’s leadership and
support slum upgrading. Environment and Urbanization,
25(1), pp. 31–45.
Depuru, S., Wang, L. & Devabhaktuni, V., 2011.
Electricity theft: Overview, issues, prevention and a
smart meter based approach to control theft. Energy
Policy [Special Section on Offshore wind power
planning, economics and environment], 39(2), p.
1007–1015.
Dobson, S., Nyamweru, H. & Dodman, D., 2015. Local
and participatory approaches to building resilience
in informal settlements in Uganda. Environment and
Urbanization, 27(2), pp. 605–620.
Dodman, D., Colenbrander, S. & Archer, D., 2016b.
Towards adaptive urban governance. In: D. Archer,
S. Colenbrander & D. Dodman, eds. Responding to
Climage Change in Asian Cities: Governance for a
More Resilient Urban Future. London, UK: Routledge.
Dodman, D., Leck, H., Rusca, M. & Colenbrander, S.,
2016a. African Urbanisation and Urbanism: Implications
for risk accumulation and reduction. Urban Africa Risk
Knowledge (UrbanARK): Working Paper 10.

IIED WorkIng papEr
www.iied.org 29
Dodman, D. & Mitlin, D., 2013. Challenges for
community-based adaptation: Discovering the potential
for transformation. Journal of International Development,
25(5), pp. 640–659.
Duranton, G., 2008. Cities: Engines of Growth and
Prosperity for Developing Countries? Washington DC,
USA: World Bank.
Euromonitor International, 2013. The World Economic
Factbook. London, UK: Euromonitor International.
Floater, G. et al., 2014. Cities and the New Climate
Economy: the transformative role of global urban
growth. London, UK: Global Commission on the
Economy and Climate.
Foster, A. & Kumar, N., 2011. Health effects of air quality
regulations in Delhi, India. Atmospheric Environment,
45 (9), pp. 1675–1683 .
Garg, A., 2011. Pro-equity effects of ancillary benefits of
climate change policies: A case study of human health
impacts of outdoor air pollution in New Delhi. World
Development, 39(6), pp. 1002–1025.
Garg, P., 2012. Energy Scenario and Vision 2020 in
India. Journal of Sustainable Energy & Environment,
Volume 3, pp. 7–17.
Goulder, L. & Williams, R. I., 2012. The choice of
discount rate for climate change policy evaluation.
Climate Change Economics, 30(4), p. 1250024 (18
pages) .
Gouldson, A., Colenbrander, S., Sudmant, A., Godfrey,
N., Millward-Hopkins, J., Fang, W., Zhao, X. 2015.
Accelerating Low-Carbon Development in the World’s
Cities. London, UK: Global Commission for the
Economy and Climate.
Grossa, R., Blyth, W. & Heptonstall, P., 2010. Risks,
revenues and investment in electricity generation: Why
policy needs to look beyond costs. Energy Economics
[Policymaking Benefits and Limitations from Using
Financial Methods and Modelling in Electricity Markets],
32(4), pp. 796–804.
Hasan, A., 2006. Orangi Pilot Project; the expansion
of work beyond Orangi and the mapping of informal
settlements and infrastructure. Environment and
Urbanization, 18(2), pp. 451–480.
IEA, 2014a. Africa Energy Outlook: A Focus on Energy
Prospects in sub-Saharan Africa. Paris, France:
International Energy Agency.
IEA, 2014b. World Energy Investment Outlook 2014:
“Power Generation Investment Assumptions”. Paris,
France: International Energy Agency.
IEA, 2014c. World Energy Outlook 2014. Paris, France:
International Energy Agency.
IEA, 2015a. World Energy Outlook 2015. Paris, France:
International Energy Agency.
IEA, 2015b. India World Energy Outlook 2015. Paris,
France: International Energy Agency.
IEA, 2015c. World Energy Outlook Special Report:
Energy and Climate Change. Paris, France: International
Energy Agency.
IEA, 2015d. Projected Costs of Generating Electricity.
Paris, France: International Energy Agency.
IRENA, 2012. Renewable Energy Technologies: Cost
Analysis Series: Volume 1: Power Sector: Biomass
for Power Generation. Paris, France.: International
Renewable Energy Agency.
IRENA, 2015. Africa 2030: Roadmap for a Renewable
Energy Future. Abu Dhabi, United Arab Emirates:
International Renewable Energy Agency.
Kennedy, C. et al., 2015. Energy and material flows of
megacities. Proceedings of the National Academy of
Sciences of the United States of America, 112(19), pp.
5985–5990.
McGranahan, G., Balk, D., Martine, G. & Tacoli, C.,
2013. Fair and Effective Responses to Urbanization
and Climate Change: Tapping Synergies and Avoiding
Exclusionary Policies. In: G. Martine & D. Schensul, eds.
The Demography of Adaptation to Climate Change.
New York, USA; London, UK; Mexico City, Mexico:
UNFPA, IIED and El Colegio de México, pp. 24–40.
McGranahan, G., Schensul, D. & Singh, G., 2016.
Inclusive urbanization: Can the 2030 Agenda be
delivered without it?. Environment and Urbanization,
28(1), pp. 13–34.
Milanovic, B., 2016. Global Inequality: A New Approach
for the Age of Globalization. Boston, USA: Harvard
University Press.
Mitlin, D. & Satterthwaite, D., 2013. Urban Poverty in
the Global South: Scale and Nature. New York, USA:
Routledge.
Mitlin, D. & Walnycki, A., 2016. Why is water still
unaffordable for sub-Saharan Africa’s urban poor?
London, UK: International Institute for Environment and
Development.
Modi, V., McDade, S., Lallement, D. & Saghir, J., 2006.
Energy and the Millennium Development Goals. New
York, USA: United Nations Development Programme,
UN Millennium Project, and World Bank.
Moss, T. & Gleave, M., 2014. Benefits and Costs of the
Energy Targets for the Post-2015 Development Agenda:
Post-2015 Consensus. Copenhagen, Denmark:
Copenhagen Consensus Centre.

Cities as e ngin es of eConomiC growth | The case for providing basic infrasTrucTure and services in urban areas
30 www.iied.org
Nordhaus, W., 2007. A review of the Stern Review on
the Economics of Climate Change. Journal of Economic
Literature, 45(3), pp. 686–702.
Patel, S., Baptist, C. & d’Cruz, C., 2012. Knowledge
is power – informal communities assert their right to
the city through SDI and community-led enumerations.
Environment and Urbanization, 24(1), pp. 13–26.
Payne, G., Durand-Lasserve, A. & Rakodi, C., 2009. The
limits of land titling and home ownership. Environment
and Urbanization, 21(2), pp. 443–462.
Pegels, A., 2010. Renewable energy in South Africa:
Potentials, barriers and options for support. Energy
Policy [Carbon Emissions and Carbon Management in
Cities], 38(9), pp. 4945–4954.
Pereira, M., Freitas, M. & da Silva, N., 2010. Rural
electrification and energy poverty; empirical evidences
from Brazil. Renewable and Sustainable Energy
Reviews, 14(4), pp. 1229 –1240.
Satterthwaite, D., 2003. The Millennium Development
Goals and urban poverty reduction: great expectations
and nonsense statistics. Environment and Urbanization,
15(2), pp. 179–190.
Satterthwaite, D., 2008. Editorial: The social and
political basis for citizen action on urban poverty
reduction. Environment and Urbanization, 20(2), pp.
307–318.
Satterthwaite, D., 2013. The political underpinnings
of cities’ accumulated resilience to climate change.
Environment and Urbanization, 25(2), pp. 381–391.
Satterthwaite, D., 2016. Missing the Millennium
Development Goal targets for water and sanitation in
urban areas. Environment and Urbanization, 28(1), pp.
9 9 –118 .
Satterthwaite, D., Hardoy, J., Pandiella, G., Johnson,
C., Colenbrander, S., Archer, D., Brown, D., Evangelina
Filippi, M. 2016. Why Resilience Matters to the Urban
Poor. Washington DC, USA: World Bank.
Satterthwaite, D., Huq, S., Pelling, M., Reid, H., Romero
Lankao, P. 2009. Adapting to climate change in urban
areas: The possibilities and constraints in low- and
middle-income nations. In: J. Bicknell, D. Dodman & D.
Satterthwaite, eds. Adapting Cities to Climate Change:
Understanding and Addressing the Development
Challenges. London, UK: Earthscan, pp. 3–50.
Satterthwaite, D. & Mitlin, D., 2014. Reducing Urban
Poverty in the Global South. Oxon, UK: Routledge.
Seto, K. et al., 2014. Human Settlements, Infrastructure
and Spatial Planning. . In: O. Edenhofer, et al. eds.
Climate Change 2014: Mitigation of Climate Change.
Contribution of Working Group III to the Fifth
Assessment Report of the Intergovernmental Panel
on Climate Change. Cambridge, United Kingdom:
Cambridge University Press.
Smith, P., 2012. The potential for investment in
Indonesia’s geothermal resource. International Journal
of Engineering Technology, 2(2), pp. 300–307.
Smith, T., 2004. Electricity theft: a comparative analysis.
Energy Policy, 32(18), p. 2067–2076.
Spence, M., Annez, P. & Buckley, R., 2009. Urbanization
and Growth. Washington DC, USA: World Bank.
Stern, N., 2006. The Economics of Climate Change:
The Stern Review. Cambridge, UK: Cambridge
University Press.
Stiglitz, J., 2012. The Price of Inequality: How Today’s
Divided Society Endangers Our Future. New Yor k ,
USA: W.W. Norton.
Sudmant, A., Millward-Hopkins, J., Colenbrander, S.
& Gouldson, A., 2016. Low carbon cities: is ambitious
action affordable? Climatic Change, pp. 1–8.
Sutherland, C. et al., 2014. Water and sanitation
provision in eThekwini Municipality: a spatially
differentiated approach. Environment and Urbanization,
26(2), pp. 469–488.
Tacoli, C., McGranahan, G. & Satterthwaite, D.,
2008. Urbanization, Poverty and Inequity: Is Rural-
Urban Migration a Poverty Problem, or Part of the
Solution?. In: The New Frontier: Urbanization, Poverty
and Environment in the 21st Century. London, UK:
Earthscan, pp. 37–54.
Turok, I. & McGranahan, G., 2013. Urbanization and
economic growth: the arguments and evidence for
Africa and Asia. Environment and Urbanization, 25(2),
pp. 465–482.
UCLG, 2010. Local Governments in the World; Basic
Facts on 96 Selected Countries. Barcelona, Spain:
United Cities and Local Governments.
UN DESA, 2014. 2014 Revision of World Urbanization
Prospects. New York, USA: United Nations Department
of Economic and Social Affairs.
UNFCCC, 2015. Adoption of the Paris Agreement.
Paris, France, United Nations Framework Convention on
Climate Change.
UN-Habitat , 2016. World Cities Report 2016:
Urbanization and Development: Emerging Futures.
Nairobi, Kenya: United Nations Human Settlements
Programme.
United Nations Development Programme, 2015. Goal 7:
Affordable and clean energy. [Online] Available at: http://
www.undp.org/content/undp/en/home/sdgoverview/
post-2015-development-agenda/goal-7.html [Accessed
8 9 2016].

IIED WorkIng papEr
www.iied.org 31
Unruh, G. & Carrillo-Hermosilla, J., 2006. Globalizing
carbon lock-in. Energy Policy, 34 (10), p p . 1185 –1197.
Ward, B., 1976. The Home of Man. New York, USA: W
W Norton.
Watson, V., 2009. ‘The planned city sweeps the poor
away…’: Urban planning and 21st century urbanisation.
Progress in Planning, 72(3), p. 151–193.
Wenban-Smith, H., 2006. Urban infrastructure: density
matters, not just size. Research Papers in Environmental
& Spatial Analysis, Volume Working Paper 104.
Wilkinson, P., Smith, K., Joffe, M. & Haines, A., 2007.
A global perspective on energy: health effects and
injustices. The Lancet, 370(9591), p. 965–978.
World Bank, 1991. Urban Policy and Economic
Development: An Agenda for the 1990s. Washington
DC, USA: World Bank.
World Bank, 2012. Addressing the electricity access
gap. Washington, DC, USA.: World Bank.
World Bank, 2014. Electric power transmission and
distribution losses (% of output). [Online] Available at:
http://data.worldbank.org/indicator/EG.ELC.LOSS.ZS
[Accessed 19 08 2016].
World Bank, 2016a. http://pubdocs.worldbank.org/
en/764161469470731154/CMO-2016-July-forecasts.
pdf, Washington, D.C., USA: World Bank.
World Bank, 2016b. CO2 emissions (metric tons per
capita). [Online] Available at: http://data.worldbank.org/
indicator/EN.ATM.CO2E.PC [Accessed 8 9 2016].
World Bank, 2016b. GDP per capita (current US$).
[Online] Available at: http://data.worldbank.org/indicator/
NY.GDP.PCAP.CD [Accessed 22 08 2016].
World Nuclear Association, 2016. The Economics of
Nuclear Power, s.l.: World Nuclear Association.
Yeager, K., 2001. Electricity development for a
sustainable world: bridging the digital divide. Buenos
Aires, Argentina: World Energy Council 18th Congress.
Related reading
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Available from: http://pubs.iied.org/17156IIED/

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Urbanisation offers substantial opportunities to reduce
poverty, in part because it is more cost-effective to
meet many basic needs in cities than in rural areas. This
paper demonstrates that providing electricity to the 200
million urban residents who currently lack access would
require only $1.37 billion per year to 2045. Generating
this electricity from low-carbon options (consistent with
avoiding a 2°C temperature rise) would cost only 1%
more. This demonstrates that relatively small amounts of
resources need to be mobilised to deliver basic services
and infrastructure to the urban poor – an essential
precursor to inclusive and sustained economic growth.
This research was funded by UK aid from the
UK Government, however the views expressed
do not necessarily reflect the views of the UK
Government.