assessing rural energy sustainability in developing countries

Energy for Sustainable Development 19 (2014) 15–28

Contents lists available at ScienceDirect

Energy for Sustainable Development

Assessing rural energy sustainability in developing countries

Brijesh Mainali a,b,⁎, Shonali Pachauri b, Narasimha D. Rao b, Semida Silveira a

a Energy and Climate Studies, Department of Energy Technology, Royal Institute of Technology, KTH, Stockholm, Swedenb International Institute for Applied Systems Analysis, Laxenburg, Austria

⁎ Corresponding author. Tel.: +46 8 790 74 31.E-mail addresses: [email protected], brijes

(B. Mainali).

0973-0826/$ – see front matter © 2014 International Enehttp://dx.doi.org/10.1016/j.esd.2014.01.008

a b s t r a c t
a r t i c l e i n f o
Article history:Received 22 September 2013Revised 14 January 2014Accepted 17 January 2014Available online 21 February 2014

Keywords:Rural household energySustainability indexIndicatorsPrincipal component analysis

Providing sustainable energy access is one of the most critical global challenges. This paper introduces a methodfor evaluating the status and progress of rural household energy sustainability in developing countries using anew composite indicator, the energy sustainability index (ESI). The ESI combines 13 techno-economic, environ-mental and social indicators of sustainability using principal component analysis (PCA). We apply the ESI toChina, India, South Africa, Sri-Lanka, Bangladesh and Ghana between 1990 and 2010. The analysis suggeststhat South Africa's rural energy sustainability index is highest followed by China, Sri Lanka, India, Bangladeshand Ghana respectively. All the countries' rural energy sustainability has improved relatively over time exceptGhana's. Improvements result mainly from increasing rural electricity use and increasing access to clean and ef-ficient cooking fuels.

© 2014 International Energy Initiative. Published by Elsevier Inc. All rights reserved.

Introduction

There is an urgent need to provide access to reliable and cleancooking energy to 2.8 billion and electricity to 1.2 billion people in theworld (World Bank, 2013). Sustainable energy is defined as energythat is reliable, affordable, and accessible and that meets economic, so-cial and environmental needs within the overall developmental contextof society, but with equitable distribution in meeting those needs(Davidson, 2002; ICSU, 2007). Providing sustainable energy has been apriority for governments throughout the world particularly since theUN Conference on Environment and Development held in Rio 1992,and the signature of the United Nations Framework Convention on Cli-mate Change (Carrera andMack, 2010). However, providing sustainablerural energy access remains one of the central political challenges formany developing countries (Mainali and Silveira, 2013).

Various bilateral and multilateral agencies started adopting sustain-ability criteria in their development models after the publication of theBrundtland (1987) report. The UN-CSD (1996) developed more than130 indicators including the four primary dimensions of sustainable de-velopment—social, economic, environmental, and institutional. Further,in 2001 (revised in 2007), a comprehensive report was published onkey sustainable development themes and sub-themes with guidelineson developing indicators of sustainable development at the nationallevel (UN, 2001; UNDESA, 2007). These large sets of indicators providedinsights on sustainable development issues, but were difficult to com-bine into measurable and quantifiable sustainability indicators(Kemmler and Spreng, 2007). Several studies have assessed energy

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sustainability using different sets of indicators and different approaches(Mainali, 2014). Energy sustainability can be evaluated in terms of amore general sustainability index, which could be useful to informpolicymakers, investors, and analysts about energy situations (Afganet al., 2005; Brown and Sovacool, 2007; Doukas et al., 2012; Edigeret al., 2007; WEC. World Energy Trilemma, 2012). However, there isno single commonly accepted method for assessing and combining alldimensions of sustainability (Ilskog, 2008; Mata et al., 2011). The IAEA(2005) presents a comprehensive list of thirty energy indicators for sus-tainable development (EISD). Some of these indicators are broad in na-ture, and hard to quantify (Ugwu and Haupt, 2007). InternationalEnergy Agency (IEA) developed the Energy Development Index (EDI)for 80 different countries, to assist policymakers in following progressmade towards modern energy access provision WEO. World EnergyOutlook (2012). However, this index only provides a snapshot at acountry level and does not cover technical and environmental aspectsof sustainability. The EDI analyzes data at the national level and com-prises indicators related mainly to access to clean cooking fuels, andelectricity at the household level and access to energy for communityservices and productive uses. The WEC. World Energy Trilemma(2012) presents an energy sustainability index (ESINational) covering in-dicators related to energy equity, security and environmental sustain-ability at an aggregated national level. Information and assessments ofrural energy sustainability are thus limited (Doukas et al., 2012).There is typically a significant imbalance in socio-economic develop-ment between rural and urban areas. The majority of rural populationsliving in developing countries are energy poor1. Thus, there is a need for

1 The energy poor are defined as those who lack access to adequate, reliable, affordableclean energy carriers and technologies for meeting energy needs mainly for cooking andthose enabled by electricity to assist in human development (Pachauri and Rao, 2013).

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16 B. Mainali et al. / Energy for Sustainable Development 19 (2014) 15–28

analyzing rural energy separately, using indicators that can provide suf-ficient insight into the sustainability of rural energy development.

This paper introduces a method for evaluating the status and prog-ress of rural household energy sustainability in developing countriesusing a new composite indicator, the energy sustainability index (ESI).Thirteen indicators (techno-economic, environmental and social) ap-propriately designed to capture rural energy sustainability have beencombined using principal component analysis (PCA) to construct theESI. We apply the analysis to six different countries including the fastdeveloping countries China, India, South Africa, and other developingcountries Sri-Lanka, Bangladesh and Ghana.

The rest of the paper is organized as follows: Section 2 highlights theprofiles of the six countries assessed. Section 3 discusses the methodol-ogy adopted in the study, theoretical framework, indicators selectionand data sources used for the analysis. Section 4 presents an evalua-tion of the selected indicators for all the countries under assessment.Results and discussions are presented in the fifth section afterperforming multivariate analysis to estimate an ESI. The analysis isfurther extended by presenting the results of a sensitivity and de-composition analysis. Conclusions are drawn in the final section.This paper contributes towards answering the broader question:‘how can we provide sustainable energy access to a large populationin developing countries?’ The study should help policy makers andplanners in the studied countries to better appreciate the sustain-ability performance of rural energy.

Profiles of the countries under assessment

The typical energy end-uses considered for determining householdenergy access are cooking and lighting, which are treated as basic ener-gy needs (Balachandra, 2011). The use and reliance on different types offuels for meeting household energy demand depend upon geographicallocation, resource conditions and economic situation. The sustainabilityassessment in this paper has been made choosing developing countriesin Asia and Sub-Saharan Africa as these are the regions where the ener-gy access problem is more acute. Countries like Bangladesh, China,Ghana, India, Sri Lanka and South Africa were selected for the analysisbased on data availability and with the intent of capturing a broadrange of development and energy conditions.

Table 1 shows some key development indicators for these countries.They span a range of rural population sizes from 15 to 1300 million; ur-banization rates of 27% to 64%; HDI from 0.47 to 0.66; and rural GDP(per cap) from $220 to $1363. The range of energy conditions in thesesix cases illustrates the extent of variation seen across developing coun-tries (see Fig. 1).

By 2010, the dependency on biomass for energy ranged from around90% in Bangladesh to around 30% in South Africa. The types of biomassused for cooking depend on geography, culture and climate conditions.Aside from wood and agricultural residues, which are used in all coun-tries, coal use is common in China and South Africa; charcoal inGhana, kerosene or paraffin in Ghana, South Africa, India; and dung inIndia. LPG (liquefied petroleum gas) is the modern form of cookingfuel used in all countries, but its use is very limited and mainly foundamong the richer population. Rural electrification rates in 2010 were

Table 1HDI, GDP and population trend of countries under study.

Description Bangladesh China

Human development index—2010 0.469 0.663Human development rank—2010 129 89Rural population in 1990 in million 89.59 817.87Rural GDP per capita in 1990 (USD of 1990) 164 230Rural population in 2010 in million 118.45 739.20Rural GDP per capita in 2010 (USD of 1990) 220 920Urbanization—2010 31% 45%

Source: IIASA, 2013; HDR, 2010.

as low as 27% in Ghana, but over 98% in China and 72% in Sri Lanka.Bangladesh and India have recently made modest improvements to in-crease rural electricity access to 42.5% and 47.5% respectively.

Methodology, theoretical framework, and data sources

Methodology for constructing composite indices

Composite indicators (CIs) are used to measure multi‐dimensionalconcepts that cannot be captured by a single indicator, such as sustain-ability (EC, 2005). A composite indicator should be constructed on thebasis of (i) a solid theoretical framework that can define the phenome-non beingmeasured, (ii) a comprehensive process of construction (cov-ering the selection of indicators and aggregation process) and, (iii) goodquality of essential data (EC, 2005; OECD, 2008). The first step in theconstruction of CIs is to build a theoretical framework defining energysustainability (see the Theoretical framework section). Then, a set of in-dicators that capture energy sustainability as defined within this theo-retical framework are selected (see the Selection of indicators section)and quantified (see Evaluating the indicators section). Finally, overallprogress towards sustainability is estimated in terms of a composite en-ergy sustainability index (ESI). The ESI is generated by aggregating theabove individual indicators usingmultivariate techniques, such as prin-cipal component analysis (PCA). EC (2005) and OECD (2008) guidelineshave been referred in constructing the composite indicator in this study,paying attention to avoid the shortfalls typically associated with CIs.

Themain advantage of PCA is that once patterns in a large number ofinterrelated data sets are recognized, the dimensionality of such datasets is reduced and compressed, without much loss of information(Helena et al., 2000; Jolliffe, 2003; Li et al., 2012). Besides, it is a non-parametric analysis and allocates weights on the basis of their statisticalsignificance. This makes the analysis neutral and independent of theusers (Ali, 2008). For this reason, a PCA technique has been applied inconstructing the ESI in this study. However, PCA does not allow takinga-priori knowledge about the indicators/variables into account. Thiscan be taken as the limitation of this technique. Besides, it is sensitiveto the number of observations andworks betterwith large sets of obser-vations. The individual indicators that do not change with others mayhave minimum contribution to the whole (OECD, 2008). Also, PCA issensitive to alterations in the basic data and updates (e.g. addition ofcountry information as new observations) (EC, 2005). Details regardingPCA technique can be found in Appendix I and in Doukas et al. (2012).

Theoretical framework

The choice of an appropriate conceptual framework and correspond-ing indicators largely depends on the specific purpose of the analysis(Fiksel et al., 2012) i.e. how we define rural energy sustainability inthis specific context. The framework offers a basis for the selection of in-dicators and their combination into a meaningful composite indicatorunder a fitness-for-purpose principle (EC, 2005; OECD, 2008). As men-tioned earlier, sustainability is often linkedwith three pillars (economic,environmental and social) (IAEA, 2005; Mahat, 2004). Two additionaldimensions (technical and institutional) are sometimes added when

Ghana India Sri Lanka South Africa

0.467 0.519 0.658 0.597130 119 91 11010.29 638.54 13.60 19.04

261 240 370 134315.50 803.59 15.01 17.21

275 522 698 136340% 30% 27% 64%

Fig. 1. Share of primary household energy by type of fuel, and electricity access rate in rural areas of selected countries (Source: BIDS, 2004; Foysal et al., 2012; Hassan et al., 2012; HIES-China, 1999; Meisen and Cavino, 2007; Zhang et al., 2009; GLSS, 2006; NSSO, 2002, 2007; Davis, 1998; HIES–SA, 2006; OECD/IEA, 2010; UNDP, 2009).

17B. Mainali et al. / Energy for Sustainable Development 19 (2014) 15–28

analyzing sustainability specifically associated with the rural sector(Brent and Rogers, 2010; Ilskog, 2008; Mainali, 2014). Although the in-stitutional dimension is important, it is hard to measure and quantify.Therefore, this dimension is often not included in sustainability indica-tors (IAEA, 2005).

ESI for the rural household sector measures the relative capability ofa country (among other observations) for providing access to secureand affordable clean energy to rural households meeting economic, so-cial and environmental needs within the overall development contextof society, and with equitable distribution (adopted from Davidson,2002; ICSU, 2007). For further elaboration of this definition, each sus-tainability dimension has been expanded according to different themesto capture the sustainability objective. A number of indicators were se-lected to assess and evaluate the different dimensions of energy sustain-ability as shown in Fig. 2. Each of these themes and the selection ofcorresponding indicators are discussed further in the Selection ofindicators section.

Selection of indicators

The selection of indicators in this studywasmade against the criteriaidentified by the OECD (2003, 2008) viz. (i) analytical soundness (i.e. it

Fig. 2. Conceptual energy sustainability framewor

has scientific/theoretical basis), (ii) measurability (i.e. through data thatare readily available, quantifiable and updated periodically), (iii) coun-try coverage (i.e. data are available and comparable across countries),and (iv) ability to describe sustainability phenomenon of household en-ergy in the rural context. Based on the above conceptual frameworkpresented in the Theoretical framework section and using the abovecriteria, appropriate sets of indicators capturing various sustainabilitydimensions and themes were selected. A large number of studies (seeTable 2 for details) on energy sustainability indicators were reviewedas part of the process. Some of these indicators were redefined tomake them more appropriate measures in the rural energy context.Thefinal sets of selected indicatorswith their descriptions are presentedin Table 2.

The linkage of modern energy access to development and the en-vironment is well-known and considered a central element in thedebate on sustainable development (IAEA, 2005; IEA, 2011; Ilskogand Kjellström, 2008; Rehman et al., 2012; UNSD, 1991). Accessibil-ity (to electricity and clean cooking energy) is therefore an impor-tant theme to be captured in a sustainability assessment. Even inplaces where there is access to electricity and clean fuels, huge in-equalities may persist in the distribution of the energy resources.This is a common syndrome of the rural household energy sector in

k (*Indicator codes are explained in Table 2).

image of Fig.�2

Table 2Selected indicators for energy sustainability analysis of rural sector.

Dimension Code Name of indicator Theme Description (units) References

Social SOI01 Share of rural population withoutelectricity

Accessibility Ratio of rural population without electricity to the total ruralpopulation (%)

1,2, 3, 4, 8, 11–16, 20, 26

SOI02 Share of population without cleanenergy

Ratio of households (or population) using traditional solid fuelsfor their cooking and other household energy uses

1,3, 4, 8, 11–16, 20, 26

SOI03 Disparity in electricity distribution Disparity Ratio of electricity use of lower quintile to electricity use of upperquintile

17, 21, 26

SOI04 Disparity in clean energydistribution

Ratio of clean fuels use of lower quintile to cleans fuels use of upperquintile

8, 17, 21

Economic ECI01 Electricity use per capita Per capita use Ratio of total final electricity consumption to total population(kWh per capita)

1, 3–7

ECI02⁎ Share of renewable energy inelectricity generation

Renewability Contribution of renewable energy generation in the total ruralelectricity supply (%)

1, 3, 4, 6–9, 11–14, 16,18–19,

ECI03 Share of household income spent onfuels and electricity

Affordability Ratio of household income spent on fuels and electricity to the totalhousehold income (%)

1–3, 8,10, 12, 13, 22, 26

ECI04⁎ Net energy import dependency Security of supply Ratio of energy imports to total primary energy supply (%) 1–4, 8, 12, 13, 26Technical TEI01 Cooking/heating energy conversion

efficiencyEnd use efficiency Ratio of consumption of final energy to the energy demanded by the

users in useful energy (%)24,25,27,28

TEI02⁎ Transmission/distribution losses(T&D losses)

Delivery efficiency Line losses during transmission and distribution of the electricity (%) 8, 36

Environmental ENI01 GHG emissions from energyproduction and use per capita

Global impact Annual GHG emissions from energy production and use per capita(Kg/Capita)

1,2, 6,8,11, 14–16, 18, 19,23, 26–30, 35–47

ENI02⁎ Impact of household air pollution(HAP) from energy systems

Local impact Disabled adjusted life years per 1000 people (DALYS/1000 person) 1,2, 11,18, 24–27, 35, 36,40, 43–45, 47

ENI03⁎ Annual rate of change in forest area Extent of forest land (%) 1

1IAEA, 2005; 2 Fecher, 2003; 3IEA, 2011; 4UNSD, 1991; 5 IEA/OECD/Eurostat, 2004; 6 Tsai, 2010; 7Doukas et al., 2012; 8 Vera and Abdalla, 2005; 9 Dimitrijevic and Salihbegovic, 2012;10Neves and Leal, 2010; 11UNDESA, 2007; 12WEO. World Energy Outlook, 2012; 13IEA, 2012; 14Ilskog and Kjellstrom, 2008; 15 Vithayasrichareon et al., 2012; 16Ilskog, 2008; 17Andrichet al., 2013; 18Kemmler and Spreng, 2007; 19Tanguay et al., 2009; 20WEC, 2000;21Pachauri and Spreng, 2011; 22Schipper et al., 1985; 23Bhattacharyya, 2012; 24Wang et al., 2009;25Wang et al., 2008a; 26Dimakis et al., 2012; 27 Wang et al., 2008b; 28Beccali et al., 2003; 29Mamlook et al., 2000; 30Mamlook et al., 2001; 31 Akash et al., 1998; 32Mohsen and Akash,1997; 33Chatzimouratidis and Pilavachi, 2009; 34Dinca et al., 2007; 35Chatzimouratidis and Pilavachi, 2008; 36Prete et al., 2012; 37Cavallaro and Ciraolo, 2005; 38EEA, 2003; 39IPCC,2001; 40Moldan et al., 2012; 41Pilavachi et al., 2009; 42Afgan and Carvalho, 2002; 43Afgan and Carvalho, 2004; 44Afgan and Carvalho, 2008; 45Begic, and Afgan, 2004; 46Pilavachi et al.,2006; 47Liposcak et al., 2006.Note: Data used is specific for the rural sector other than those marked with *.⁎ Indicators based on national average data and used here as a proxy to the rural sector.


developing countries (Daioglou et al., 2012; Mainali et al., 2012).This disparity (in electricity and energy use) is mainly due to unevenincome distribution, a limitation to welfare improvement.

Affordability is one of the main barriers affecting rural people's pos-sibility to have reliable modern energy access (Abdullah andMarkandya, 2012). Affordability can be captured by the share of energyexpenditure in total disposable income (IAEA, 2005). The security of theenergy supply is also one of the important objectives and a sustainabil-ity criterion for many countries. Disruptions in energy supply can causehuge financial and economic losses and can also hamper the marketpenetration of energy resources. Net energy import dependency of thenation is one way of capturing energy security (IAEA, 2005; IEA,2010). The share of renewable energy sources in electricity generationprovides ameasure of the degree of cleanliness and self-reliance in elec-tricity generation.

Electricity has multiple uses and is crucial for socio-economic devel-opment of rural households. It is difficult to distinguish the amount ofelectricity that is used for lighting, other consumptive uses and produc-tive uses such as a micro-enterprise conducted within the home(OECD/IEA, 2012). However, previous studies have shown a positive re-lation between household electricity use and income for poor popula-tions (Rao, 2013). A significant positive correlation has been observedbetween per capita electricity consumption and human development(including health, education and income) particularly in countries withlow to medium human development (Gomez and Silveira, 2010;Pasternak, 2000). A recent study for developing countries has shownthat a 1% increase in per capita electricity consumption increases theHDI by 0.22% (Ouedraogo, 2013) Therefore, increase in the per capitaelectricity consumption can be expected to have a positive impact onrural sustainability in developing countries. All the four aspects discussedabove are crucial from the economic point of view.

Per capita energy use for cooking/heating might be high or low de-pending upon geo-climatic conditions. Therefore, access to modern

fuels and end use efficiency (i.e. conversion efficiency of stoves) aremore relevant indicators of sustainability than the amount of energyuse per capita. Different types of fuels used for cooking/heating inhouseholds have different conversion efficiencies. From a technical per-spective, the overall conversion efficiency of fuels used for heating andcooking should be maximized for efficient utilization of resources.Also, transmission and distribution (T&D) losses of electricity are nor-mally high in rural areas because of long transmission lines and wide-spread extension of low-voltage distribution networks to extensiveareaswithout proper planning (Rahman et al., 2013). This causes signif-icant power losses and poor quality of electricity supply. So, minimizingT&D losses is important for energy sustainability. Capturing these twoissues in a sustainability assessment is important from a technicalpoint of view.

The combustion of traditional biomass fuels and coal in traditionalstoves at poor efficiency poses a significant public health hazard, pre-dominantly affecting rural women and children (IAEA, 2005; Kemmlerand Spreng, 2007; UNDESA, 2007; Wang et al., 2009; WHO, 2002).The over exploitation of fuelwood may also lead to deforestation inrural areas. But this impact is location specific. Apart from this, the con-version of tropical forests to agricultural land and other uses has causeddeforestation. Forests are not only important to provide fuelwood inrural areas, but also to protect soil and water, to conserve biodiversityand to provide various social services (FAO, 2010). Besides, deforesta-tion can have an impact on river flow patterns, which can impactmicro or pico hydro energy resources. Thus, the percentage change inforest area is an indicator that can capture an important aspect ofrural energy sustainability. Similarly, greenhouse gas emissions due tothe production of electricity from different sources and the burning ofdifferent fuels in households have global impacts via climate change.Considering these local and global environmental impacts due to theproduction and utilization of different type of fuels is crucial to a sus-tainability assessment (IAEA, 2005; Tsai, 2010; UNDESA, 2007).

2 In case where there are no gaseous fuels used by both quintiles, we have looked at theuse of liquid fuels viz. use of kerosene,which is relatively cleaner than the use of solid fuelsin traditional stoves.

Table 3Data Sources.

Data Source References

Rural household energy consumptionand energy expenses

• Nationally representative household surveys (rural data) from variousnational official and ministerial publications, household income andexpenditure surveys (HIES) and living standard surveys

• Various other reports and journal publications (with case studies coveringdifferent provinces/districts)

HIES-Bangladesh, 2010; HIES-China, 1999; GLSS, 2006;NSSO, 2002, 2007, 2010; HIES–SA, 2006;

Barnes et al., 2011; Davis, 1998; Foysal et al., 2012; Hassanet al., 2012; Rajmohan and Weerahewa, 2007; Khandkeret al., 2012; Vermaak et al., 2009; Zhang et al., 2009

Population using solid fuels in rural areas World Health Organization's Household Energy Database WHO, 2010Transmission distribution losses; net energyimports

World Bank database World Bank, 2013

GHG emissions from electricity generation Domestic electricity emission factors IEA, 2007GHG emissions from cooking fuels Standard emission factors IPCC, 2006Health impacts of household air pollution The Global Burden of Disease (GBD) country profiles GBD, 2010Rate of change in the forest land areas The Global Forest Resources Assessment report FAO, 2010


Initially, we aimed at covering all five sustainability dimensions(economic, social, environmental, technical and institutional) withsome sets of indicators for each dimension. However, due to data con-straint, not all the desired sets of indicators could be included in theanalysis. For example, indicators measuring (i) the institutional ar-rangements for the distribution of modern fuels in rural areas, such asnumber of LPG supply depots per 1000 people or (ii) the financialsoundness of power utilities in providing rural energy services couldhave further strengthened the analysis of the institutional dimension.However, such information was not readily available for all countriesconsidered in the analysis. In fact, issues related to the institutional di-mension are difficult to measure in quantitative terms (IAEA, 2005).Thus, our analytical framework is limited to four dimensions. Giventhe scarcity of comparable quantitative data across the countries, thechoice of indicators was ultimately also driven by available information.From a pragmatic point of view, the theoretical framework, set withinthe four sustainability dimensions in this analysis, could provide asound basis for analyzing rural energy sustainability.

Data sources

The availability and authenticity of rural energy data is a major chal-lenge in most developing countries (Pachauri and Cherp, 2011; Zhanget al., 2009). In this study, we sourced data from various national officialand ministerial publications, living standard surveys, various reports,journal publications and data banks of various international organiza-tions. All data sources used are indicated in Table 3. The study aimedat examining the sustainability trend over different timeframes(1991–1995, 1996–2000, 2001–2005 and 2006–2010) to capture theprogress made over the last two decades. However, it was not possibleto gather data from these countries for all the timeframes. We gathereddata for at least two different timeframes for each country to analyze thesustainability trends. In the case of India and China, analysis has beendone over three different timeframes.

Evaluating the indicators

The progress made towards rural energy sustainability can be mea-sured in terms of the above discussed sustainability indicators. In thissection, we evaluate these indicators for all the countries underassessment.

Accessibility

The two energy poverty related indicators associated with energyaccess: (i) population without access to electricity and (ii) the popula-tion relying on the traditional use of solid fuels for cooking have beenevaluated in this section.

Fig. 3 shows that China and Sri Lanka havemade significant progressin providing access to electricity. China has already achieved near

universal access while Sri Lanka is fast progressing. But switching thepopulation from solid fuels to modern clean cooking fuels has been amajor challenge in both countries. The situation in Sri Lanka is evenmore challenging as about 85% of its rural population still relies onsolid fuels. Ghana's situation is the weakest amongst all in terms of pro-viding access to electricity and clean cooking fuels. Compared to theother countries, South Africa has made remarkable progress in termsof providing energy access. The population relying on solid fuels has de-creased from 57% to 37% in the last 20 years. However, about 45% of therural population in South Africa still has no access to electricity. Indiaand Bangladesh's progress is relatively slow, both in terms of providingaccess to electricity and clean fuels.

Disparity

Disparities in energy use between countries or within a countrymayresult from highly uneven income distribution, energy distribution net-works, andmajor geographical differences among regions (IAEA, 2005).Large inequalities also exist in the distribution of electricity and othercooking fuels (in terms of availability)within the same rural communityand such disparity is mostly related to unequal distribution of income(Daioglou et al., 2012; Siddiqi, 1995). Rich rural households typicallyuse more electricity and have better access to modern energy thanpoor rural households (Pachauri and Jiang, 2008). In order to capturethe disparity in energy use across various income groups, we havelooked at the ratio of (i) per capita of electricity consumed by thepoorest quintile (R1) to the richest quintile (R5) of the rural populationand, (ii) the amount of clean energy (gaseous fuel)2 consumed by thepoorest quintile (R1) to the richest quintile (R5) for all the countrycases over different time periods.

The ratios calculated in Table 4 have a negative relation with dispar-ity and a positive relation with sustainability. The analysis revealed ahuge disparity in the electricity distribution among the poor and therich, and the disparity has been increasing in most of the countries (ex-cept Sri Lanka and India) over time. The distribution is highly uneven inGhana and relatively less uneven in Bangladesh. The disparity in the useof clean fuels for cooking was even higher than that in electricity useand seems to be increasing over the time. The disparity in the use ofclean fuels was highest in India followed by Sri Lanka and China.

Per capita electricity use

A significant positive relation exists between the amount of electric-ity consumed and human development achieved, specifically in coun-tries with low to medium human development (Alam et al., 1991;

Fig. 3. Percentage of rural population using solid fuels and without access to electricity.


Pasternak, 2000). Electricity use per capita in rural households has beenassessed here in this section.

As shown in Fig. 4, the per capita electricity consumption in rural areasvaries significantly across countries. South Africa has the highest(496 kWh) while Ghana has the lowest (26 kWh) per capita electricityconsumption in recent years. Compared to other emerging economies,the per capita electricity consumption in India is relatively low (5 timeslower than South Africa and 4 times lower than China). The per capitaelectricity consumption has risen over time in all countries,which is likelydriven by income growth.

Table 4Electricity and clean cooking fuel distribution across poorest and richest income quintiles(disparity measurement).

Country Period Electricity consumed Clean fuels consumed by

Affordability

Accessible energy is not useful if it is not affordable. The energy shareof the household budget, which is also referred to as the ‘energy bur-den’, is one measure of affordability (Winkler, 2007). This is an impor-tant aspect to capture the sustained affordability of fuels andelectricity (see Table 5). Limited income (=limited affordability) mayend up in households using traditional fuels in an inefficient way evenif they have access to cleaner technologies and fuels (IAEA, 2005). Thepoor normally spend a huge share of their income on electricity andcooking fuels (Mainali et al., 2012; Pachauri and Jiang, 2008). As incomerises, the share of expenses on fuels and electricity decreases in relativeterms.

The energy expenditure share is relatively low in Sri Lanka. One ofthe reasons for this low share of energy expenditure in the householdbudget in Sri Lanka could be seen as the availability of free, reliableand high calorific firewood (Rajmohan and Weerahewa, 2007;Wickramasinghe, 2011).

by R1 to R5 R1 to R5

Bangladesh 2001–2005 0.58 0.582006–2010 0.21 0.40

China 1996–2000 0.27 0.052001–2005 0.24 0.062006–2010 0.24 0.06

Ghana 1990–1995 0.08 0.722006–2010 0.05 0.25

India 1996–2000 0.13 0.062001–2004 0.16 0.012001–2005 0.16 0.01

Sri Lanka 1996–2000 0.12 0.042001–2005 0.17 0.04

South Africa 1990–1995 0.38 0.482006–2010 0.14 0.26

Renewability and security of supply

Increasing the share of renewable energy in electricity generation isa way to diversify electricity supply sources and increase energy inde-pendence, while also reducing greenhouse gas emissions. We assumethat the percentage of net energy imported at the national level alsohas proportionate impacts on energy security of rural areas. We, there-fore, assessed the net energy import or export dependencies of thesecountries over time as a sustainability indicator capturing the securityof supply. The status of the countries on these two indicators is present-ed in Table 6. Notably, the import of fuels has increased significantly in

Ghana due to increased demand and limited internal refinery capacity(Mohammed et al., 2013; Osei et al., 2013).

Delivery and end use efficiency

Delivery efficiency refers to the efficiency of electricity delivery(average T&D losses). High T&D losses increase the cost of rural elec-trification and pull up the electricity tariffs (OECD/IEA, 2010; Pengand Pan, 2006). The overall conversion efficiency of fuels used forcooking has been captured by end use efficiency. Since electricalend use appliances are diverse and it is difficult to track their effi-ciency, this has not beenmeasured. The overall conversion efficiencyfor cooking is measured as the ratio of the sum of useful energies percapita (estimated using per capita final energy of each fuel type, andaccounting for stove efficiency and stove penetration) to the sum oftotal final energy per capita consumed for cooking. Table 7 shows theT&D losses and the estimated end use efficiency (cooking fuels con-version efficiency) of the cooking fuels in different time periods.

Local and global environmental impacts

Household air pollution (HAP) due to burning of fuels has many ad-verse health impacts. The impacts depend upon the fuels and stove

image of Fig.�3

Fig. 4. Per capita electricity use in rural households of six developing countries studied.


types, room ventilation and exposure time. Exposure to HAP from solidfuels causes many diseases, including acute and chronic respiratory dis-eases, asthma, tuberculosis, cardiovascular disease etc. (WHO, 2007).This is the leading environmental cause of disease according to the newGlobal Burden of Disease (GBD) study (Lim et al., 2012). This is one ofthe local impacts of fuel use and is normally expressed in terms of thedisability-adjusted life year (DALYs). DALYs per 1000 people attributableto HAP across different countries and over different time frames are tab-ulated in Table 8. With a realization of the importance of forests, coun-tries are putting their efforts on afforestation and reforestation tomaintain or even increase forest areas. The net percentage change in for-est covered land areas in all countries studied is also shown in Table 8.

Greenhouse gas emissions from energy use in the residential sectorare a global environmental concern. If we only consider GHG emissions,as includedwithin theKyoto Protocol, the burning of biomass in the res-idential sector has lower GHG emissions compared to kerosene or LPGprovided that the biomass is renewably harvested (Edwards et al.,2004). Standard GHG emission factors published by IPCC (2006) forcooking fuels and emission factors published by IEA (2007) for domesticelectricity have been used in this analysis for estimating GHG emis-sions3 (in terms of CO2 equivalent taking CO2, CH4 and N2O into ac-count) per capita (see Table 8).

Results and discussion

In this section, we construct the ESI of the rural household sectorusing PCA. The index is used to analyze the overall progress made to-wards rural energy sustainability by different countries over differenttimeframes. A sensitivity analysis is also performed to assess the robust-ness of the results. The analysis is further extended by carrying out a de-composition analysis of the composite indicator.

Multivariate analysis

The normalized value of all the indicators is presented in AppendixII. In the next step, the underlying nature of the normalized data vari-ables and their interrelationship is analyzed. Multivariate analysis tech-niques like principal components analysis (PCA) (See Appendix I for thePCA computation steps) reveal how different variables change in rela-tion to each other and how they are associated. This is instrumentalfor obtaining insight on the data structure and helpful in the compres-sion of the dataset without losing information (Helena et al., 2000;Jolliffe, 2003; OECD, 2008).

3 We have used a conservative assumption of 90% of the biomass being renewablyharvested.

The data normalization scales all variables in the range from zero toone. The corresponding Eigenvectors and Eigenvalues along with theloading factors for all variables associated with first four factor axes(F1 to F4) are presented in Table 9 (See Appendix I for PCA details).The first two factors F1 and F2 together contribute 62% of the variability.With an additional two factors F3 and F4, i.e. the four factors togetherrepresent around 88% of the variances of the original set of matrix.The general practice is to keep enough factors to account for 80% to90% of the variation (OECD, 2008). Hence, the two factor spaces(F1 and F2) and (F3 and F4) have been chosen for this analysis, asthese retainmost of the information without losing significant informa-tion from the original variables.

The variables can be plotted as points in a correlation circle withinfactor space using their loadings as coordinates. The correlation circle(Fig. 5) shows the projection of the initial variables in the two factorspaces. In the case when all data sets are perfectly represented by onlytwo factor axes, the sum of the squared loadings is equal to one. Insuch cases, all the projections of variables will fall on the circumference.However, most often complex phenomenon like sustainability requiresmore than 2 factor axes to represent the data sets completely. Undersuch conditions, the projection of some of the variables will be situatedinside the correlation circle. Those projections that are closer to the cir-cumference are better represented by the first two factor axes. The var-iables that are close to the circumference and close to each other aresignificantly positively correlated (r close to 1). The variables that areorthogonal to each other are not correlated (r close to 0). If the variablesare on opposite sides of the center, then they are significantly negativelycorrelated (r close to−1). If the projections of the variables are close tothe center, then they are not represented by this correlation circle and,in such cases, their projections need to be looked at in another correla-tion circle with the other two factor axes. For example: a positive corre-lation is observed between the impact from HAP and the share ofpopulation using solid fuels in the correlation circle (Fig. 5 factor spaceF1 and F2). The hazardous gasses and particulates emitted due to the in-efficient burning of solid fuels have serious health impacts on exposedwomen and children. So, the correlation is as anticipated. On the otherhand, the variable “GHG emission” is negatively correlated with“cooking conversion efficiency”. The improvement in the conversion ef-ficiency could be because of technological improvements in cookingstoves or due to a transition to cleaner fuels. Not all transitions reduceGHG emissions, especially if the transition is from renewed biomass tofossil fuels. Indicators like “net energy import dependency” and “shareof population without electricity access” are almost orthogonal to eachother indicating hardly any correlation between them. Indicators like“disparity in the electricity distribution” and “share of household in-come spent on fuels and electricity” are insignificant in the factor

image of Fig.�4

Table 5Share of household income spent on fuels and electricity (energy burden) in rural areas.

Country Period Percentage of income spenton fuel and electricity

Bangladesh 2001–2005 6.102006–2010 6.06

China 1996–2000 6.102001–2005 6.942006–2010 7.50

Ghana 1990–1995 5.122006–2010 7.64

India 1996–2000 8.072001–2005 9.962006–2010 8.00

Sri Lanka 1996–2000 1.002001–2005 1.42

South Africa 1990–1995 6.772006–2010 6.73


space of F1 and F2, but their presence is significant in the factor space ofF3 and F4.

The correlation circle (Fig. 5) along with the loading factors inTable 9 helps to understand the linkage of the original variables withthese factor axes. The variables for examples “electricity use per capita”,“cooking energy conversion efficiency”, “GHG emission”, “share of pop-ulation using solid fuel”, “annual change rate in the forest area”, “shareof population without electricity”, and “household indoor air pollution”aremainly associatedwith factor axis F1whereas “disparity in clean en-ergy distribution” and “net energy import dependency” are associatedwith factor F2 (The numbers in bold in Table 9 correspond for each var-iable to the factor for which the squared cosine is the largest). The indi-cators “share of renewables in electricity” and “share of householdincome spent on fuels and electricity” are mainly associated with factorF3 and “disparity in electricity distribution” is associated with factor F4.The variables/indicators which have large variations across the observa-tions (among countries and/or over time) and higher correlation withother variables/indicators are loaded in the first factor (F1) and so on.The indicators associated with the first factor and having the largestsquared cosine (loading factor) are influential in determining the sus-tainability index. The top four indicators are: “electricity use per capita”,“cooking energy conversion efficiency”, “share of population use solidfuels” and “GHG emission from energy production/use per capita.”

After estimating Eigenvalues, Eigenvectors and loading factors, theprincipal components (PCs) can be computed (using Eq. 3 described inAppendix I). Then the energy sustainability index (ESI) for rural house-hold sector can be calculated (using Eq. 7 described in Appendix I).

Table 6Renewable energy share in electricity generation and percentage of net energy import inselected countries.

Country Period Share of renewable energy inthe total rural electricity supplied

Percentage of netenergy importsa

Bangladesh 2001–2005 3.97 19.12006–2010 3.14 16.5

China 1996–2000 16.65 −0.042001–2005 15.53 2.62006–2010 16.00 7.7

Ghana 1990–1995 100.00 2.92006–2010 68.01 21.7

India 1996–2000 16.95 16.12001–2005 15.66 20.12006–2010 15.38 24.3

Sri Lanka 1996–2000 46.00 38.42001–2005 39.00 45.1

South Africa 1990–1995 0.45 −28.72006–2010 1.80 −16.5

a Negative sign represents the net export.

Energy sustainability index

Fig. 6 shows that among the six countries assessed, South Africa hasthe highest ESI over the timeframes examined. China ranked second inthis sustainability analysis followed by Sri Lanka, India, Bangladeshand Ghana. All the countries' ESI improved in recent years exceptGhana's.

Among the countries analyzed, the per capita rural electricity con-sumption is highest in South Africa and consumption has doubled inthe last 20 years. South Africa has managed to reduce the share ofrural population using solid fuels from 57% in the early 1990s to 37%in recent years, which is impressive compared to the other developingnations. In terms of security of energy supply, South Africa has negativenet energy imports till date. It has also been successful in restricting T&Dlosses within 6% to 8% even after expanding the grid network to remoterural areas. Also the cooking energy conversion efficiency is relativelyhigh compared to other countries. All these factors have contributedto South Africa ranking first in this sustainability assessment.

The White Paper on Energy Policy 1998 gave a strong thrust to theSouth African government's efforts in the energy sector. The energypolicy was designed to achieve sustainability, the key objectives beingto (i) increase access to affordable energy services, (ii) enhance energygovernance, (iii) stimulate economic development, (iv) secure energysupply, and (v) minimize health and environmental impacts associatedwith energy use (Winkler, 2007). The household energy strategy inSouth Africa had a strong focus on household electrification (Barneset al., 2009). A “poverty tariff” i.e. free basic electricity (FBE) of50 kWh per month to poor households was introduced in 2003 for ex-tending the social benefits of electrification to the economically weakersection of the population. A study has shown that the FBE scheme hashad a significant impact on the overall consumption of electricity andhas also encouraged appliance ownership and usage (Davis et al.,2008). Energy policies after 2000 have set clear targets and a specifiedtime-span to achieve those targets.

Ghana, in contrast, has been facing difficulties in providing sustain-able energy to larger sections of its population. The per capita electricityconsumption and access to electricity and clean fuels have been steadilyincreasing in Ghana, though at a slower pace in comparison to the othercountries assessed. In the period 1990–1995, since Ghanawas at the ini-tial stage of its rural energy service expansion, T&D losses, energy im-ports, and inequity in the access to clean fuels were lower comparedto the other countries. However, in the process of energy service expan-sion, these factors have significantly increased over time; alongwith de-clining forest reserves (Kemausuor et al., 2011).

All these factors have resulted in a lower rural ESI for Ghana com-pared to the other countries. Ghana is the only nation in this studythat did not experience rural energy sustainability improvement during

Table 7Cooking fuels conversion efficiency and transmission/distribution losses in selectedcountries.

Country Period Cooking fuels conversionefficiency (%)

T&D line losses (%)

Bangladesh 2001–2005 13.46 28.402006–2010 13.78 22.00

China 1996–2000 19.32 6.972001–2005 19.83 6.782006–2010 27.53 6.21

Ghana 1990–1995 13.36 3.342006–2010 14.70 21.72

India 1996–2000 15.22 22.612001–2005 16.05 26.712006–2010 17.90 22.61

Sri Lanka 1996–2000 17.44 18.452001–2005 18.54 17.76

South Africa 1990–1995 22.51 7.042006–2010 25.46 8.86

Table 8Local and global environmental impacts.

Country Period Impact of household air pollution (HAP)from energy use (DALYs per 1000 person)

Annual change rate in forest area (%) GHG emissions from energy use (Kg-CO2 eq.per capita)

Without electricity Including electricity

Bangladesh 2001–2005 22.63 −0.18 327 3652006–2010 19.39 −0.18 272 309

China 1996–2000 15.41 1.2 498 7262001–2005 15.38 1.75 761 10162006–2010 15.28 1.39 925 1235

Ghana 1990–1995 29.49 −1.99 298 2982006–2010 22.76 −2.19 258 262

India 1996–2000 36.37 0.22 99 1532001–2005 31.63 0.70 193 2182006–2010 27.75 0.21 193 229

Sri Lanka 1996–2000 18.85 −1.20 132 1342001–2005 18.69 −1.47 175 199

South Africa 1990–1995 23.25 0.00 284 5152006–2010 6.52 0.00 81 535


the period analyzed. Despite several policymechanisms in place, inade-quate investments in the sector, weak institutional framework, as wellas poor policy implementation and resource management have beenidentified as persistent challenges faced by the country (Kemausuoret al., 2011; MOE-Ghana, 2010).

WEC. World Energy Trilemma (2012) indicates that Ghana's energyperformance in terms of energy security and social equity has seen a de-creasing trend in recent years. But, the same report also indicates an in-creasing trend in the contextual performance in terms of political andsocial strength. This positive indication and recent evidences suggestthat Ghana is well on its way to achieve higher rural energy sustainabil-ity in the future. In fact, the Ghana Sustainable Energy for All (SE4ALL)Action Plan was the first Country Action Plan to be developed in re-sponse to the UN Secretary General's initiative (SE4ALL-Ghana, 2012).

Decomposition and sensitivity analysis

Adecomposition analysis is important to determine the contributionof each subcomponent or set of indicators to the aggregated compositeindicator (OECD, 2008). First, we have calculated the ESI for the threekey dimensions of indicators on their own: (i) techno-economic indica-tors (ii) social indicators and, (iii) environmental indicators. The analy-sis has been further extended by carrying out a decomposition analysislooking at the countries' scores on the key indicators associated withfactor axes F1 and F2. Furthermore, there is a strong correlation be-tween the positive influence of per capita electricity growth, and thecorresponding negative but dominant influence on GHG emissions

Table 9Computed eigenvectors (EV), eigenvalues and loading factors (LF).

Variables F1

EV LF

Electricity use per capita 0.968 0.936Share of renewables in electricity −0.552 0.305Net energy import dependency 0.624 0.389Share of household income spent on fuel and electricity −0.216 0.047Share of population without electricity 0.685 0.469Share of population using solid fuels 0.892 0.795Disparity in electricity distribution 0.246 0.061Disparity in the clean energy distribution −0.257 0.066GHG emissions from energy production and use per capita −0.854 0.730Impact of household air pollution from energy systems 0.686 0.471Annual change rate in the forest area 0.733 0.537Cooking energy conversion efficiency 0.901 0.812Energy losses (T&D) 0.630 0.397Eigen Values 6.016Cumulative variability (%) 46.3

Note: Numbers in bold correspond for each variable (indicator) to the factor for which the squ

resulting from this electricity consumption growth. In the third subsec-tion, we evaluate the results under a different interpretation of GHGemission impact that excludes GHG emissions from electricity.

ESI with techno-economic, social and environmental indicatorsFig. 7 shows an ESI including only (i) techno-economic indicators;

(ii) social indicators and (iii) environmental indicators, which are thencompared to the composite ESI. The sustainability on these various di-mensions is different for different countries. Among the six countries,South Africa has the highest techno-economic as well as environmentalsustainability in recent years, whereas China has the highest sustain-ability in the social dimension and this has increased over time.Bangladesh has the lowest techno-economic sustainability followed byGhana in recent years. Ghana's sustainability in the social as well as en-vironmental dimensions is the lowest. India's sustainability in the socialdimension is also relatively low. However, India's position is relativelyhigher on the environmental dimension. Though China hasmade signif-icant progress on the techno-economic and social dimensions, its envi-ronmental sustainability has decreased over time. Sri Lanka has been inthemid-range compared to the others and its techno-economic and so-cial sustainability has risen over time, though its environmental sustain-ability has decreased in the recent years.

Decomposition analysis of ESIWehave further extended the analysis by looking at the performance

of the three emerging economies in recent years on the indicators asso-ciated mainly with the first and second principal axes. Fig. 8 shows that

F2 F3 F4

EV LF EV LF EV LF

0.069 0.005 0.098 0.010 −0.091 0.0080.059 0.004 0.693 0.480 −0.270 0.0730.724 0.524 −0.019 0.000 −0.198 0.039

−0.263 0.069 0.649 0.422 0.608 0.370−0.650 0.423 0.085 0.007 0.100 0.010

0.147 0.022 0.122 0.015 −0.111 0.0120.355 0.126 −0.500 0.250 0.697 0.4850.892 0.796 0.158 0.025 0.261 0.068

−0.015 0.000 −0.041 0.002 −0.019 0.000−0.115 0.013 0.396 0.157 0.426 0.181−0.176 0.031 −0.559 0.312 −0.062 0.004−0.082 0.007 0.180 0.032 −0.060 0.004

0.288 0.083 0.590 0.359 −0.140 0.0202.101 2.070 1.275

62.4 78.4 88.2

ared cosine is the largest.

Fig. 5. Correlation circle in the factor space of (F1 and F2) and (F3 and F4).


South Africa has the highest values on four indicators i.e. highest energyper capita,minimumshare of population using solid fuels, lowest impactof household air pollution, and negative net import dependency. Besides,South Africa has relatively less disparity in terms of clean energy distri-bution compared to China and India. On the other hand, China has thelowest share of population without electricity and high cooking energyconversion efficiency. India's performance is the lowest among thethree countries on all these aspects except its per capita GHG emissionsfromenergy production and its use are lower. China's GHGemissions arethe highest among the three countries. The area coverage under theradar chart in Fig. 8 is proportionate to each nation's overall rural energysustainability.

Fig. 6. Energy sustainability index of various

Sensitivity analysis of GHG emissionsAs mentioned earlier, electricity use per capita as an economic indi-

cator has a positive impact on sustainability. On the other hand, electric-ity generation may lead to increase in GHG emissions depending uponthe fuel mix in the generation system, which has a negative impact onsustainability. Therefore, a further sensitivity analysis was carried outaccounting for (i) GHG emissions from all types of fuels including elec-tricity and (ii) GHG emissions excluding electricity.

Fig. 9 does not showmuch change in sustainability trends over timethough there are some changes in terms of value of the indicators/index.The contribution of GHG emissions in the overall ESI is about 12% whenwe consider emission from all household fuels including electricity.

countries across different timeframes.

image of Fig.�5

image of Fig.�6

Fig. 7. Energy sustainability index covering (i) Techno-economic (ii) Social (iii) Environmental and (iii) Overall indicators.


GHG emission is among the top four indicators contributing to the ESI(Electricity use per capita being the first with a 15.5% contribution) inthis case. However, the contribution of GHG emissions in the overallESI shrinks to half (i.e. around 6%) when only emissions from cookingenergy are included. In this case, the role of GHG emissions becomesless significant in determining the ESI.

Conclusion

This paper has introduced a method for evaluating rural energysustainability in the form of a composite indicator, the “energy sustain-ability index” and applied it to six countries (China, India, South Africa,Sri-Lanka, Bangladesh and Ghana). The ESI assesses the relative statusand trends in these countries when sustainable energy access in ruralareas is considered.

The analysis shows that rural energy sustainability was the highestin South Africa (in 2006–2010) among the various observations forthe six countries. China ranked second followed by Sri Lanka, India,Bangladesh and Ghana. The relatively high rural energy sustainabilityin South Africa could be associated with its commitment towards sus-tainability, clear policy goals, and ability to achieve such goals within aspecified timeframe. The ESI of all the countries improved over time ex-cept in the case of Ghana. This trend was not consistent across all

Fig. 8. Radar diagram presenting decomposition of various key indicators associated with factotakes the value 100, and the lowest is 0).

component dimensions. For instance, China's progress on environmen-tal indicators worsened, while Ghana's improved. The study has identi-fied four key indicators which influence the results most: (i) increasingrural electricity use (ii) increasing access to modern fuels, (iii) efficientuse of cooking energy and (iv) minimizing per capita GHG emissionsfrom energy generation and use.

The ESI introduced in this paper incorporates sustainability indica-tors that encompass the techno-economic, social and environmental as-pects of the rural household energy context. This includes energyaccessibility, electricity use, disparity in energy use among rich andpoor households, affordability, delivery and end use efficiency, renew-ability, security of supply and local and global environmental impacts.It aggregates them into a composite index by weighting them on thebasis of their statistical variation and significance over time. Other ener-gy indices, for example, EDI give snapshots of more limited sets of indi-cators at the national level mainly related to access to clean cookingfuels, and electricity. EDI has been constructed by aggregating the aver-age of normalized values of the indicators. So, ESI introduced in thispaper gives more insight when it comes to the evaluation of rural ener-gy sustainability,

Institutional factors and their relative significance in rural energysustainability have not been assessed in this study, but future researchin this direction could help explain the causes of the trends observed

rs F1 and F2 in China, India and South Africa. (The highest performance for each indicator

image of Fig.�7

image of Fig.�8

Fig. 9. Sensitivity of ESI with GHG emissions accounting (i) emissions from fuels + electricity and (ii) emissions from all fuels excluding electricity.


in this study. Another direction for future work is to account for differ-ent development stages of countries in comparing time trends.

This method has some caveats. It does not allow for user-definedweighting of indicators. The precision of composite indicators increasesalong with improvements in data collection. Since data for all the indi-cators were not available on an annual basis, analyses have been madeusing 5 year timeframes. Further efforts to improve data collection;identify new data sources and enhance the international comparabilityof statistics can help to track the performance at regular intervals, pref-erably on an annual basis. This will enhance the relevance and applica-bility of such analyses. As data availability improves, this methodwouldoffer even better insights when applied to larger sets of countries.

Acknowledgment

This paper was written in the scope of the research program atEnergy and Climate Studies Division, KTH and a project funded by theSwedish International Development Cooperation Agency (SIDA). Thepaper was done in collaboration with the International Institute forApplied Systems Analysis (IIASA), Austria. We are also grateful to KICInno-Energy for the financial support to cover mobility costs during aresearch visit to IIASA.

Appendix A. Supplementary data

Supplementary data to this article can be found online at http://dx.doi.org/10.1016/j.esd.2014.01.008.

References

Abdullah S, Markandya A. Rural electrification programmes in Kenya: policy conclusionsfrom a valuation study. Energy Sustain Dev 2012;16:103–10.

Afgan NH, Carvalho MG. Sustainability assessment of a hybrid energy system. Energy Pol-icy 2008;36:2903–10.

Afgan NH, Carvalho MG. Sustainability assessment of hydrogen energy systems. Int J Hy-drogen Energy 2004;29:1327–42.

Afgan NH, Carvalho MG. Multi-criteria assessment of new and renewable energy powerplants. Energy 2002;27:739–55.

Afgan NH, Carvalho MG, Hovanov NV. Modeling of energy system sustainability index.Therm Sci 2005;9:3–16.

Akash BA, Mamlook R, Mohsen MS. Multi-criteria selection of electric power plants usinganalytical hierarchy process. Electr Power Syst Res 1998;52:29–35.

Alam MS, Bala BK, Huq AMZ, Martin MA. A model for the quality of life as a function ofelectrical energy consumption. Energy 1991;16:739–45.

Ali HMM. Development of Arab water sustainability index using principal componentanalysis. The 3rd International Conference on Water Resources and Arid Environ-ments; 2008.

Andrich MA, Imberger J, Oxburgh ER. Inequality as an obstacle to sustainable electricityand transport energy use. Energy Sustain Dev 2013;17:315–25.

Balachandra P. Dynamics of rural energy access in India: an assessment. Energy 2011;36:5556–67.

Barnes B, Mathee A, Thomas E, Bruce N. Household energy, indoor air pollution and childrespiratory health in South Africa. J Energy South Afr 2009;20:4–13.

Barnes DF, Khandker SR, Samad HA. Energy poverty in rural Bangladesh. Energy Policy2011;39:894–904.

BIDS. Rural energy survey database. Dhaka, Bangladesh: Bangladesh Institute of Develop-ment Studies; 2004.

Beccali M, Cellura M, Mistretta M. Decision-making in energy planning. Application of theElectre method at regional level for the diffusion of renewable energy technology.Renew Energy 2003;28:2063–87.

Begic F, Afgan N. Sustainability assessment tool for the decision making in selection of en-ergy system-Bosnian case. Energy 2004;32:1979–85.

Bhattacharyya S. Energy access programmes and sustainable development: a critical re-view and analysis. Energy Sustain Dev 2012;16:260–71.

Brent AC, Rogers DE. Renewable rural electrification: sustainability assessment ofmini-hybrid off-grid technological systems in the African context. Renew Energy2010;35:257–65.

Brown MA, Sovacool BK. Developing an ‘energy sustainability index’ to evaluate energypolicy. Interdisc Sci Rev 2007;32:335–49.

Brundtland G, editor. Our common future, the World Commission on Environment andDevelopment (WCED). UK: Oxford University Press; 1987.

Carrera D, Mack A. Sustainability assessment of energy technologies via social indicators:results of a survey among European energy experts. Energy Policy 2010;38:1030–9.

Cavallaro F, Ciraolo L. A multi-criteria approach to evaluate wind energy plants on an Ital-ian island. Energy Policy 2005;33:235–44.

Chatzimouratidis AI, Pilavachi PA. Multicriteria evaluation of power plants impact onthe living standard using the analytic hierarchy process. Energy Policy 2008;36:1074–89.

Chatzimouratidis AI, Pilavachi PA. Technological, economic and sustainability evaluationof power plants using the Analytic Hierarchy Process. Energy Policy 2009;37:778–87.

Daioglou V, Ruijven BJ, Vuuren DP. Model projections for household energy use in devel-oping countries. Energy 2012;37:601–15.

Davidson O. Sustainable energy and climate change: African perspectives. In: Davidson O,Sparks D, editors. Developing energy solutions for climate change: South African re-search at EDRC. Cape Town: Energy and Development Research Centre; 2002.p. 145–52.

Davis M. Rural household energy consumption. The effects of access to electricity evi-dence from South Africa. Energy Policy 1998;26:207–17.

Davis S, Hughes A, Louw K. The impact of free basic electricity on the energy choices oflow income households: a case study in South Africa. Working paperThe Programon Energy and Sustainable Development, Stanford University; 2008.

Dimakis AA, Arampatzis G, Assimacopoulos D. Monitoring the sustainability of the Greekenergy system. Energy Sustain Dev 2012;16:51–6.

Dimitrijevic Z, Salihbegovic I. Sustainability assessment of increasing renewable energysources penetration — JP Elektroprivreda B&H case study. Energy 2012;47:205–12.

Dinca C, Badea A, Rousseaux P, Apostol T. A multi-criteria approach to evaluate the natu-ral gas energy systems. Energy Policy 2007;35:5754–65.

Doukas H, Papadopoulou A, Savvakis N, Tsoutsos T, Psarras J. Assessing energy sustain-ability of rural communities using principal component analysis. Renew Sustain En-ergy Rev 2012;16:1949–57.

EC. Tools for composite indicators building. Institute for the Protection and Security of theCitizen Econometrics and Statistical Support to Antifraud Unit I-21020 Ispra (VA)Italy; 2005.

Ediger VS, Hogor E, Surmelic N, Tatlıdil H. Fossil fuel sustainability index: an application ofresource management. Energy Policy 2007;35:2969–77.

Edwards RD, Smith KR, Zhang J, Ma Y. Implications of changes in household stoves andfuel use in China. Energy Policy 2004;32:395–411.



http://refhub.elsevier.com/S0973-0826(14)00011-8/rf0005











































































image of Fig.�9


EEA. Greenhouse gas emission trends and projections in Europe. EEA Environmental issuereport no. 36, 42. Copenhagen, Denmark: European Environment Agency (EEA);2003665–80 [Environment].

FAO. Global Forest Resources Assessment 2010. Main report. Rome: Food and AgricultureOrganization of United Nation; 2010.

Fecher RS. Indicators of sustainability for the energy sector: a South African case study.Energy Sustain Dev 2003;1:35–49.

Fiksel J, Eason T, Frederickson H. A framework for sustainability indicators at EPA. Nation-al Risk Management Research Laboratory Office of Research and Development U.S.Environmental Protection Agency; 2012.

Foysal MA, Hossain ML, Rubaiyat A, Sultana S, Shin MY. Household energy consumptionpattern in rural areas of Bangladesh. Indian J Energy 2012;5:72–85.

GBD. Global burden of disease country profiles. WA 98121, USA: Published by The Insti-tute for Health Metrics and Evaluation; 2010.

GLSS. Ghana Living Standards Survey 2005/06. Ghana: Ghana Statistical Service; 2006.Gomez M, Silveira S. Rural electrification of the Brazilian Amazon — achievements and

lessons. Energy policy 2010;38:6251–60.Hassan MK, Pelkonen P, Halder P, Pappinen A. An analysis of cross-sectional variation in

energy consumption pattern at the household level in disregarded ruralBangladesh. J Basic Appl Sci Res 2012;2:3949–63.

HDR. HumanDevelopment Report 2010. The RealWealth of Nations: Pathways to HumanDevelopment. UNDP; 2010.

Helena B, Pardo R, Vega M, Barrado E, Fernandez JM, Fernandez L. Temporal evolution ofgroundwater composition in an alluvial aquifer (Pisuerga river, Spain) by principalcomponent analysis. Water Res 2000;34:807–16.

HIES-Bangladesh. Household Income and Expenditure Survey 2010. Bangladesh Bureauof Statistics, Statistics Division, Ministry of Planning; 2010.

HIES-China. Chinese Rural Household Budget Survey's Yearbook 1999. National Bureau ofStatistics (NBS), Bejing: China Statistics Publishing House; 1999.

HIES–SA. Income and expenditure of households 2005/2006: statistical release. StatisticsSouth Africa; 2006.

IAEA. Energy indicators for sustainable development: guidelines and methodologies.Vienna: International Atomic Energy Agency (IAEA); 2005.

ICSU. Sustainable energy in sub-Saharan Africa. The International Council for Science(ICSU), Regional Office for Africa; 2007.

IEA. Understanding energy challenges in India, policies, players and issues. 75739 ParisCedex 15, France: International Energy Agency, 9 rue de la Fédération; 2012.

IEA. Voluntary reporting of greenhouse gases. Appendix F. Electricity emission factors.USA: Energy Information Administration; 2007.

IEA. World energy outlook. http://www.worldenergyoutlook.org/resources/energydevelopment/accesstoelectricity/, 2011. [last viewed 2nd Jan 2013].

IEA. Energy balances of OECD countries, IEA statistics. International Energy Agency; 2010.IEA/OECD/Eurostat. Energy statistical manual. Paris, France: International Energy Agency;

2004.IIASA. International Institute for Applied System Analysis (IIASA) GGI Scenario Database.

Available at:http://www.iiasa.ac.at/Research/GGI/DB/, 2013.Ilskog E, Kjellström B. And then they lived sustainably ever after? — assessment of

rural electrification cases by means of indicators. Energy Policy 2008;36:2674–84.

Ilskog E. Indicators for assessment of rural electrification — an approach for the compar-ison of apples and pears. Energy Policy 2008;36:2665–73.

IPCC. IPCC guidelines for National Greenhouse Gas Inventories (IGES), Japan; 2006.IPCC. IPCC third assessment report: climate change 2001. A report of the intergovernmen-

tal panel on climate change. Geneva, Switzerland: Intergovernmental Panel on Cli-mate Change; 2001.

Jolliffe IT. Principal component analysis. New York: Springer; 2003.Kemausuor F, Obeng GY, Hammond AB, Dukerd A. A review of trends, policies and

plans for increasing energy access in Ghana. Renew Sustain Energy Rev2011;15:5143–54.

Kemmler A, Spreng D. Energy indicators for tracking sustainability in developing coun-tries. Energy Policy 2007;35:2466–80.

Khandker SR, Barnes DF, Samad HA. The welfare impacts of rural electrification inBangladesh. Energy J 2012;33:187–206.

Li T, Zhang H, Yuan C, Liu Z, Fan C. A PCA-basedmethod for construction of composite sus-tainability indicators. Int J Life Cycle Assess 2012;17:593–603.

Lim SS, Vos T, Flaxman AD, Danaei G, Shibuya K, Adair-Rohani H, et al. A comparative riskassessment of burden of disease and injury attributable to 67 risk factors and risk fac-tor clusters in 21 regions, 1990–2010: a systematic analysis for the Global Burden ofDisease Study 2010. Lancet 2012;380:2224–60.

Liposcak M, Afgan NH, Duić N, Carvalho MG. Sustainability assessment of cogenerationsector development in Croatia. Energy 2006;31:2276–84.

Mahat I. Implementation of alternative energy technologies in Nepal: towards theachievement of sustainable livelihoods. Energy Sustain Dev 2004;8:9–16.

Mainali B. Sustainability of rural energy access in developing countries. StockholmSweden: Royal Institute of Technology-KTH; 2014 [Doctoral Thesis].

Mainali B, Pachauri S, Nagai Y. Analyzing cooking fuel and stove choices in China till 2030.J Renew Sustain Energy 2012;4:031805.

Mainali B, Silveira S. Alternative pathways for providing access to electricity in developingcountries. Renew Energy 2013;57:299–310.

Mamlook R, Bilal AA, Mousa SM. A neuro-fuzzy program approach for evaluating electricpower generation systems. Energy 2001;26:619–32.

Mamlook R, Bilal AA, Salem N. Fuzzy sets programming to perform evaluation of solarsystems in Jordan. Energy Convers Manag 2000;42:1717–26.

Mata TM, Martins AA, Sikdar SK, Costa CAV, Caetano NS. Biofuels sustainability evaluationthrough the supply chain. Proceedings from the 6th Dubrovnik conference; 2011.

Meisen P, Cavino N. China energy data book, 7.0; 2007 [October 2008].

MOE-Ghana. National energy policy—2010. Republic of Ghana: Ministry of Energy; 2010.Mohammed YS, Mokhtar AS, Bashir N, Saidur R. An overview of agricultural biomass for

decentralized rural energy in Ghana. Renew Sustain Energy Rev 2013;20:15–22.Mohsen SM, Akash BA. Evaluation of domestic solar water heating system in Jordan using

analytic hierarchy process. Energy Convers Manag 1997;38:1815–22.Moldan B, Janousková S, Hák T. How to understand and measure environmental sustain-

ability: indicators and targets. Ecol Indic 2012;17:4–13.Neves AR, Leal V. Energy sustainability indicators for local energy planning: review of cur-

rent practices and derivation of a new framework. Renew Sustain Energy Rev2010;14:2723–35.

NSSO. Household consumption of various goods and services in India. Round 66. Nationalsample survey. GoV India: Ministry of Statistics and Programme Implementation;2010.

NSSO. Unit level data from the Household Consumer Expenditure Survey, Round 55. Na-tional Sample Survey Organisation. GoV India: Ministry of Statistics and ProgrammeImplementation; 2002.

NSSO. Unit level data from the Household Consumer Expenditure Survey, Round 61. Na-tional Sample Survey Organisation. GoV India: Ministry of Statistics and ProgrammedImplementation; 2007.

OECD, 2003. OECD environmental indicators: development, measurement and use[As Accessed on: 25 August 2013, from http://www.oecd.org/dataoecd/7/47/24993546.pdf].

OECD. Handbook on constructing composite indicators: methodology and user guide.OECD Publication; 2008.

OECD/IEA. Methodology for energy access analysis. World energy outlook. 75739 Paris,Cedex 15, France: International Energy Agency; 2012.

OECD/IEA. Comparative study on rural electrification policies in emerging economies.75739 Paris, Cedex 15, France: International Energy Agency; 2010.

Ouedraogo NS. Energy consumption and human development: evidence from a panelcointegration and error correction model. Energy 2013;63:28–41.

Osei G, Arthur R, Afrane G, Agyemang EO. Potential feedstocks for bioethanol productionas a substitute for gasoline in Ghana. Renew Energy 2013;55:12–7.

Pachauri S, Cherp A. Energy security and energy access: distinct and interconnected chal-lenges. Curr Opin Environ Sustain (Editorial Overview) 2011;3:199–201.

Pachauri S, Jiang L. The household energy transition in India and China. Energy Policy2008;36:4022–35.

Pachauri S, Rao N. Gender impacts and determinants of energy poverty: are we asking theright questions? Curr Opin Environ Sustain 2013;5:205–15.

Pachauri S, Spreng D. Measuring and monitoring energy poverty. Energy Policy 2011;39:7497–504.

Pasternak AD. Global energy futures and human development: a framework for analysis.U.S. Department of Energy. Lawrence Livermore National Laboratory; 2000.

Peng W, Pan J. Rural electrification in China: history and institution. China World Econ2006;14:71–84.

Pilavachi PA, Roumpeas CP, Minett S, Afgan NH. Multi-criteria evaluation for CHP systemoptions. Energy Convers Manag 2006;47:3519–29.

Pilavachi PA, Stilianos DS, Pappas VA, Afgan NH.Multi-criteria evaluation of hydrogen andnatural gas fuelled power plant technologies. Appl Therm Eng 2009;29:2228–34.

Prete CL, Hobbs BF, Norman CS, Cano-Andrade S, Fuentes A, Spakovsky MR.Sustainability and reliability assessment of microgrids in a regional electricitymarket. Energy 2012;41:192–202.

Rahman MM, Paatero JV, Lahdelma R. Evaluation of choices for sustainable rural electrifi-cation in developing countries: a multicriteria approach. Energy Policy 2013;59:589–99.

Rajmohan K, Weerahewa J. Household energy consumption patterns in Sri Lanka. SriLankan J Agric Econ 2007;9:55–77.

Rao DN. Does (better) electricity supply increase household enterprise income in India?Energy Policy 2013;57:532–41.

Rehman IH, Kar A, Banerjee M, Kumar P, Shardul M, Mohanty J, Hossain I. Understandingthe political economy and key drivers of energy access in addressing national energyaccess priorities and policies. Energy Policy 2012;4:727–37.

Schipper L, Ketoff A, Kahane A. Explaining residential energy use by internationalbottom-up comparisons. Annu Rev Energy 1985;10:341–405.

SE4ALL-Ghana. Sustainable energy for all action plan. UNDP; 2012.Siddiqi TA. Energy inequities within developing countries. An important concern in the

global environmental change debate. Glob Environ Chang 1995;5:447–54.Tanguay GA, Rajaonson J, Lefebvre JF, Lanoie P. Measuring the sustainability of cities: an

analysis of the use of local indicators. Ecol Indic 2009;10:407–18.Tsai W. Energy sustainability from analysis of sustainable development indicators: a case

study in Taiwan. Renew Sustain Energy Rev 2010;14:2131–8.Ugwu OO, Haupt TC. Key performance indicators and assessment methods for infrastruc-

ture sustainability—a South African construction industry perspective. Build Environ2007;42:665–80.

UN. Indicators of sustainable development: guidelines and methodologies. New York,USA: United Nation; 2001 [www.un/org/esa/sustdev/publications/indisd-mg2001.pdf, Accessed on 13 September 2013].

UN-CSD. Indicators of sustainable development: framework and methodologies. TheUnited Nations Commission on Sustainable Development (CSD); 1996.

UNDESA. Indicators for sustainable development. Guidelines and Methodologies Depart-ment of Economic and Social Affairs of the United Nations Secretariat, UN, New York;2007.

UNDP. The energy access situation in developing countries. A Review Focusing on theLeast Developed Countries and Sub-Saharan Africa. United Nations DevelopmentProgramme; 2009.

UNSD. Energy statistics: a manual for developing countries. New York, USA: UnitedNations Statistics Division; 1991.








































http://www.worldenergyoutlook.org/resources/energydevelopment/accesstoelectricity/

http://www.worldenergyoutlook.org/resources/energydevelopment/accesstoelectricity/




http://www.iiasa.ac.at/Research/GGI/DB/




























































http://www.oecd.org/dataoecd/7/47/24993546.pdf

http://www.oecd.org/dataoecd/7/47/24993546.pdf



















































http://www.un/org/esa/sustdev/publications/indisd-mg2001.pdf

http://www.un/org/esa/sustdev/publications/indisd-mg2001.pdf












Vera I, Abdalla L. Energy indicators to assess sustainable development at the nationallevel: acting on the Johannesburg plan of implementation. Energy Stud Rev2005;14:156–72.

Vermaak C, Kohler M, Rhodes B. Developing energy-based poverty indicators for SouthAfrica. J Interdisc Econ 2009;21:163–95.

Vithayasrichareona P, MacGilla IF, Nakawirob T. Assessing the sustainability challengesfor electricity industries in ASEAN newly industrializing countries. Renew Sustain En-ergy Rev 2012;16:2217–33.

Wang JJ, Jing YY, Zhang CF, Shi GH, Zhang XT. A fuzzy multi-criteria decision-makingmodel for trigeneration system. Energy Policy 2008a;36:3823–32.

Wang JJ, Jing YY, Zhang CF, Zhao JH. Review on multi-criteria decision analysis aid in sus-tainable energy decision-making. Renew Sustain Energy Rev 2009;13:2263–78.

Wang JJ, Jing YY, Zhang CF, Zhang XT, Shi GH. Integrated evaluation of distributedtriple-generation systems using improved grey incidence approach. Energy 2008b;33:1427–37.

WEC. Energy for tomorrow's world— acting now. London, UK:World Energy Council; 2000.WEC. World Energy Trilemma. Energy sustainability index. Regency House 1–4 Warwick

Street London W1B 5LT United Kingdom: World Energy Council; 2012.

WEO. World Energy Outlook. Measuring progress towards energy for all; 20121–20[Chapter 18].

WHO. Addressing the impact of household energy and indoor air pollution on thehealth of the poor: implications for policy action and intervention measures;2002.

WHO. Indoor air pollution, national burden of disease estimates. World HealthOrganization; 2007.

WHO. WHO household energy database. Department of Public Health and Environment,World Health Organization; 2010.

Wickramasinghe A. Energy access and transition to cleaner cooking fuels and tech-nologies in Sri Lanka: issues and policy limitations. Energy Policy 2011;39:7567–74.

Winkler H. Energy policies for sustainable development in South Africa. Energy SustainDev 2007;XI:26–34.

World Bank. Global tracking framework report for sustainable energy for all. World Bank;2013.

Zhang L, Yang Z, Chen B, Chen G. Rural energy in China: pattern and policy. Renew Energy2009;34:2813–23.





































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