A multicriteria approach to sustainable energy supply for the rural poor

European Journal of Operational Research 218 (2012) 801–809

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European Journal of Operational Research

journal homepage: www.elsevier .com/locate /e jor

Innovative Applications of O.R.

A multicriteria approach to sustainable energy supply for the rural poor

Felipe Henao a, Judith A. Cherni b, Patricia Jaramillo c, Isaac Dyner c,⇑a Icesi University, Faculty of Business and Economics, 760031 Cali, Colombiab Centre for Environmental Policy, Imperial College London, London SW7 2AZ, UKc CeiBA, Universidad Nacional de Colombia, AA 1027 Medellín, Colombia

a r t i c l e i n f o a b s t r a c t

Article history:Received 9 December 2007Accepted 23 November 2011Available online 1 December 2011

Keywords:Decision analysisOR in energyMultiple criteria analysisRobustness and sensitivity analysisOR in societal problem analysis

0377-2217/$ - see front matter � 2011 Published bydoi:10.1016/j.ejor.2011.11.033

⇑ Corresponding author.E-mail address: [email protected] (I. Dyner).

Despite significant progress in energy technology, about two billion people worldwide, particularly thepoor in rural areas of developing countries, have no access to electricity. Decision-making concerningthe most appropriate energy technology for supplying these areas has been difficult; existing energy deci-sion-support tools have been useful but are mostly incomplete. Trade-offs, as well as impacts that can bepositive or negative, may emerge as a result of implementing modern forms of energy. These can affectboth community’s livelihoods as well as the confidence of decision-makers in relation to alternative tech-nologies. The paper discusses a newly designed multicriteria approach and its novel robustness analysisfor selecting energy generation systems for the improvement of livelihoods in rural areas. The proposedmethodology builds upon a sustainable rural livelihoods framework to address multiple interactions andcalculate trade-offs aimed at boosting decision-makers’ confidence in the selected technologies. Themethodology is tested via a case study in Colombia.

� 2011 Published by Elsevier B.V.

1. Introduction

Access to electricity is still limited despite the substantialworldwide power-grid expansion that took place during the1960s and 1970s in developing countries. Most of the approxi-mately two billion people that have no access to electricity arelocated in the rural areas of the developing world (Sebitosi and Pil-lay, 2005). While off-grid, and particularly renewable energy tech-nology is often the only option for remote locations, manyinstallations in rural areas have stopped functioning or are defec-tive (Cherni et al., 2007). Existing energy decision-making supporttools, together with often-deficient technical information andinappropriate financial support, have contributed to such failures.Financial and technical criteria have generally prevailed, whilethe possibility of using a conceptual framework that encompassessustainable energy development, such as this work proposes (see,e.g., Roseland, 2000; Huang et al., 1995; Srivastava and Rehman,2006), has been neglected. If provision of energy is addressed fromtechnical and financial views alone, solutions are likely to remainunsustainable and hence give little support to poverty reduction(Cherni et al., 2007). In this sense, improvements in one particularasset of the community might be achieved at the expense ofanother asset (Pohekar and Ramachandran, 2004). These concernsand limitations have motivated the design of a multicriteria deci-sion support approach that combines technical and non-technical

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criteria for the development of rural infrastructure, to promoteeffective and sustainable energy solutions by improving decision-making processes.

This paper discusses the multicriteria approach and the robust-ness mechanisms that have been designed to aid and enhance thedecision-making process for sustainable and affordable supply ofenergy in remote areas. Robustness analysis is an inbuilt assurancemechanism necessary to ‘‘strengthen the decision-making pro-cess’’. This particular feature contributes to the uniqueness andreliability of the proposed model, which is novel, provides an opti-misation assessment valid for a reasonable 15-year span and hasbeen tested with primary information gathered through a casestudy in Colombia. The current study focuses on the proposedapproach and the robustness method, and tests these in a poor re-gion in Colombia. It does not discuss policy issues or define thecharacteristics of the software that implements this methodology,as these have been partly discussed in Cherni et al. (2007).

2. Energy decision making for sustainable livelihoods

There has been increasing interest in devising single- and multi-criteria decision-making methods for modern energy uses (Whiteet al., 2010). Single criteria optimisation approaches have pro-moted rural energy schemes (e.g., Espen, 2006; Georgopoulouet al., 1997; Kablan, 1997; Pelet et al., 2005; Smith and Mesa,1996). A main drawback of single-criteria decision-making is thatphysical and socio-economic conditions of prospective technologyusers are overlooked (Nigim et al., 2004; Huang et al., 1995). For

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0.00

0.20

0.40

0.60

0.80

1.00Physical

Financial

NaturalSocial

Human

Community's pentagon (e.g. Diesel plant 10kW) Ideal pentagon

Fig. 1. SL approach to rural energy: real vs. ideal state of development under currentenergy supply conditions.

802 F. Henao et al. / European Journal of Operational Research 218 (2012) 801–809

this reason some forms of multicriteria conceptualisation andframeworks have been used for planning prioritisation of energysupply alternatives for isolated areas, including models that assessenvironmental impacts (Huang et al., 1995; Kablan, 1997; Beccalliet al., 1998; Hobbs and Meier, 2000; Mladineo et al., 1987; Geor-gopoulou et al., 2003). These include multicriteria optimisationand simulation decision support systems such as LEAP (Long RangeEnergy Alternatives Planning), MARKAL (MARKet ALlocation) andHOMER.

While these decision tools have been crucial for supporting thepromotion of off-grid and interconnected power systems, impor-tant limitations still remain in relation to: (i) the geographical scaleof the application and (ii) the participation of the rural communityin decision-making (Beer and Swanepole, 1994; Hobbs and Horn,1997; Sebitosi and Pillay, 2005); (iii) the narrow number ofsustainability dimensions encompassed (Limmeechokchaia andChawana, 2007; White and Lee, 2009); (iv) the replicability of samesolutions (Roseland, 2000); and, significantly, it is important toconsider (v) that no approach assesses the overall impact of mod-ern energy on peoples’ livelihoods. Further, while there is recogni-tion that modern energy may facilitate development, it may alsohave a negative impact on the environment or further drain scarcefinancial resources, all of which could be mitigated if technologywere carefully selected.

A main purpose for the approach discussed here is to assurethat energy generation will contribute to sustainable developmentin rural communities. To incorporate this objective, the currentmethodology draws on the sustainable livelihoods (SL) approach.The SL approach emphasises the importance of understandingthe various components and factors of livelihood. Sustainablemeans mechanisms that enable people to enhance and ensure theirlivelihoods over the long term (Ashley and Carney, 1999). The SLperspective also establishes that prospective rural beneficiarieshave not just needs but also possess human, social, physical, natu-ral and financial assets. The SL approach provides a useful guide-line to improve our understanding of people’s livelihoods andtheir relationship to available assets (Gilling et al., 2001). This arti-cle highlights the importance of energy access for the sustainableliving conditions of the rural poor. By doing so, the SL perspectiveproved appropriate and valuable, and it has been significantlyenhanced through the current focus on energy for the improve-ment of poor livelihoods.

3. A decision-support approach to providing sustainable ruralenergy

This section proposes a methodological approach to assess theimpact of alternative energy options on rural livelihoods. In the pro-posed approach, the SL pentagon is used (i) to show when the fiveowned assets have been fully developed, i.e., representing an idealpentagon; thereby the distance between the outside vertices ofthe pentagon and its centre always equals one; (ii) to display thereal level of development of assets under current energy conditions,i.e., where each vertex has a value between 0 and 1; and (iii) toreflect the dynamic effect of implementing future energy systemson the assets and livelihoods of a population (Fig. 1; for furtherdetails, see Cherni et al., 2007).

Application of different energy technologies results in variouspentagonal shapes, depending on factors such as populationfeatures, service priorities and costs, as well as on environmentalimpacts. The proposed approach seeks to identify energy alterna-tives that are most likely to positively affect the five identified as-sets. It would be expected therefore that the best technologysolution would shift the real SL pentagon closer to the ideal situa-tion. To close the gaps between the real and the ideal pentagons, a

major challenge is therefore to select energy alternatives that mayimprove the state of as many of the five assets as possible. A mostsatisfactory energy solution would thus augment the size of a reallivelihoods pentagon. Multicriteria and robustness analyses fortechnical and non-technical information are used to tackle thischallenge.

A standardised metric has been devised to calculate the extentto which modern energy supply might—positively or negatively—transform communities’ livelihoods. The metric allows estimationof the changes that would occur to every asset by establishingthe distance from the vertices to the centre of the sustainable live-lihoods pentagon (Fig. 1). Eq. (1) illustrates the effect that variousmodern energy technologies may have on each of the livelihoods’five assets (Cj (Ai), j = 1,2, . . . ,5).

CjðAiÞ ¼1

1þ e�ajXjðAiÞ; ðj ¼ 1; . . . ;5; i ¼ 1; . . . ;nÞ; ð1Þ

where Cj(Ai) represents the impact of the ith energy alternative (Ai,i = 1, . . . ,n) on asset j, j = 1,2, . . . ,5, (1 indicates Physical, 2 Financial,3 Natural, 4 Social and 5 Human assets); Cj(Ai) takes values in theinterval (0,1), and indicates how the energy option i impacts capitalj (‘‘0’’ is for the strongest negative effect or outcome of the energyalternative i on asset j, and ‘‘1’’ indicates the largest positive effecton the asset); Xj represents the set of factors that compose each as-set j (e.g., for natural capital, the factors refer to water, air, landscape,flora and fauna); Xj (Ai) represents the effects of the ith energy alter-native on the factors of the corresponding asset j. Finally, aj is anarithmetic mean function that normalises, in a common intervalfor all assets [�b,b], the effects of the ith energy option across allassets, so that it can be compared. Then, b is the largest absolute va-lue that covers all assets’ scores, which is used to standardise the Cj

function. The specific structure for each asset Xj(Ai) is beyond thescope of the current paper.

According to the designed metric, a low value (near to zero) forasset j indicates that resources are poorly developed, e.g., infrastruc-ture is very limited, or there are insufficient levels of health care and/or education services. It is argued therefore that any small improve-ment to current energy supply (i.e., an increase in Xj value) willtranslate into a significant positive impact on the assets. Conversely,where initially the value of an asset j is defined as near to one, that is,when there is a sufficient level of resources, the improvementexpected from access to new energy supply on the same asset j willbe comparatively small. Further, it is argued that the moredeveloped the initial condition of an asset j is, the larger the amountof energy required to generate a perceivable impact on the sameasset j (i.e., the Xj value). It is assumed here that assets never reach

F. Henao et al. / European Journal of Operational Research 218 (2012) 801–809 803

the theoretical maximum (i.e., Cj equal to one), nor disappear com-pletely (i.e., Cj equal to zero).

Eq. (1), above, indicates the extent of development that can beachieved when a new energy technology is implemented. A sig-moid function is employed to represent the non-linear relationshipthat has been established between the impacts of energy technol-ogy and the state of assets. The sigmoid function, which has beenparameterised for each particular asset, indicates that the level ofdevelopment of an asset increases exponentially when a newenergy option is in place. However, increments of assets declinewhen assets reach a satisfactory level. This asymptotic relationshipbetween electricity provision and the human development indexhas for long been observed in the literature (e.g. Smil, 2003). Alter-native formulations to Eq. (1) were tested and did not show signif-icant impact on the outcomes—the trials are not reported here dueto space constraints (see also Wedley and Choo, 2011).

4. Searching for best energy options

Having determined how to assess the impact of energy technol-ogy on livelihoods’ assets, the current section discusses the selec-tion of most appropriate technologies for rural communities. Theselection process is defined here as a discrete multicriteria prob-lem. The matrix [C(A)]n�5, resulting from multiplying the vectorsC by A, represents a multicriteria decision-making matrix. The ele-ments of this matrix results from assessing the impact of a technol-ogy vector A (composed of n energy technology alternatives) on theassets vector C (of five criteria corresponding to the five SL assets).The components of the Cj(Ai) represent the evaluation of alterna-tive i with respect to asset j (indicated in Eq. (1)).

Seeking to single out an effective solution, the discrete multicri-teria reference-point method, called Compromise Programming(CP) (Zeleny, 1982), is used here to gauge the gap between the esti-mated effects of different energy technologies on the livelihoodsassets, and the ideal pentagon Eq. (2). CP is an appropriate methodto identify the most satisfactory solution as the closest to the idealpentagon, and to identify a pentagon that is the largest and most-symmetric of all. CP is used here to measure the distance betweenpentagons through a family of Dp-metric functions. Other multicri-teria methods, such as PROMETHEE, could also be employed toevaluate energy options but they would not be able to representthe gap between the ideal pentagon and those generated by theimpact of energy options on the different assets. This gap betweenpentagons is one of the key features of the method and it is calcu-lated as follows (see also Zeleny, 1982; Pohekar and Ramachan-dran, 2004):

Min DPðAiÞ ¼X5

j¼1

Wpj

Cj;ref � CjðAiÞCj;ref � Cj;min

��p

!1=p

;

8<:

i ¼ 1; . . . ;n; 1 6 p 61); ð2Þ

where Dp(Ai) is the gap between the ideal pentagon and the valueresulting from modelling the implementation of the ith energy op-tion (Ai); Cj(Ai) is the evaluation of alternative i with respect to assetj; Cj,ref is the ideal value of asset j (Cj,ref = 1); Cj,min is the lowest valuegiven to asset j (Cj,min = 0); Wj is the weight of relative importanceassigned to asset j; and p is a distance parameter that reflects theattitude of the decision-maker regarding compensation betweendeviations in the pentagons (typical values for p are 1 and 2).

The multicriteria approach proposed in this paper assumes thata single decision-maker (or a consensual group of them) assignsthe weight factors Wj for all five assets. However, this process isnot straightforward (e.g. see Kodikara et al., 2010). It starts first

by determining the priorities of the community regarding the rel-ative importance of their five assets. This information is collectedfrom a household survey that is conducted on the community.After that, the swing weight method (von Winterfeldt and Edwards,1986) is followed in order to construct the decision-maker’s firstset of weight factors. Finally, the values Wj obtained will be furtherreviewed and explored by using three robustness analysis tests;this is the subject described in the next section.

Eq. (2) aims to minimise the distance (Dp) between the idealpentagon and the pentagons resulting from installing new powercapacity. The objective is to find a compromise energy solutionthat would best increase the assets of a community (representedby Cj, j = 1, . . .,5). As to the pentagons, it is expected that the mostsuitable technology will also produce the largest pentagon.

The weight factors in Eq. (2) reflect the decision-makers’ prefer-ences and trade-offs with respect to improvements in each of thefive livelihoods assets.

In summary, the proposed approach seeks to select the ith(i = 1, . . .,n) energy option (Ai) that produces the shortest distanceDp(Ai) between the energy induced and the ideal pentagon. Thetechnology option that is associated with the shortest distancefrom the ideal pentagon is the one most likely to be appropriatefor a community. Eq. (2) minimises the weighted aggregationfunction of distances between the values of all the five sustainablelivelihoods assets and the ideal value for each asset (i.e., Cj,ref = 1).

Some of the benefits of CP include that it is mathematically trac-table for decision-makers; also, it has been widely researched,extensively used, and is theoretically well-founded. It has been val-idated empirically, requires few parameters to determine the deci-sion-makers (DMs’) preferences, and produces Pareto-optimalsolutions (Ramanathan and Ganesh, 1994; Nigim et al., 2004).These are features that decision-makers value highly.

5. A proposed robustness analysis

Robustness analysis is crucial as it ultimately refines and sup-ports the decision-making process. This section discusses theanalytical mechanisms for supporting a robust energy decisionprocess for sustainable rural energy development. The robustnessapproach proposed here assesses the soundness of an energy solu-tion. It does so by using a novel sensitivity analysis that is con-ducted on the initially-estimated weights. The variation ofweights results from considering livelihoods and energy sustain-ability characteristics. The aim is to reduce the sensitivity of themulticriteria approach, proposed above, in relation to the weightfactors (see Pöyhönen and Hämäläinen, 2001) by recalculatingthem through the following proposed tests: (i) interdependencebetween assets, (ii) entropy of impacts, and (iii) avoidance ofunsuitable solutions, as indicated next. These tests do not, in anyway, interfere with the condition of independence between weightfactors, as the mechanisms proposed here are designed to informDMs about sustainable developmental issues and the benefitsand drawbacks of the available options. DMs finally determinethe value of the weight factors.

5.1. Wider impact: test of interdependence

A distinctive decision-making situation that involves multicrite-ria analysis arises when an energy technology could influence, ineither positive or negative ways, the state of current assets. Weargue that the best energy alternative should improve or enhancethe majority of the five SL assets, and thus provide a balancedsolution. In the absence of such a possibility, an interdependence testwas designed to fit the decision-makers’ goals. An analysis of‘interdependence’ aims to identify the most problematic or


conflicting asset, in the sense that its improvement through energysupply is only achieved at the expense of worsening other assets. Insuch a case, the mechanism used adjusts the weight factor and givesa lower weight of importance to the conflicting asset by assumingpreferences based on differences between pairs of alternatives; seeSalo and Hämäläinen (1997).

The relation of interdependence is positive when complemen-tary effects occur between two different assets, and is negativewhen effects are opposite. That is, interdependence between a pairof assets (i.e., Ci and Cj, i – j, i, j = 1, . . . ,5) is positive when increasesin asset Cj correspond with improvements in asset Ci, in the casethat energy alternative Ak is chosen instead of Al(k – l, k,l = 1, . . . ,n) Eq. (3). However, interdependence between a pair of as-sets (Ci and Cj, i – j, i, j = 1, . . . ,5) may be negative when Ci rises,while Cj decreases, as Ak is chosen instead of Al Eq. (4), or vice versa.

CiðAkÞ � CiðAlÞCjðAkÞ � CjðAlÞ

P 0;

k – l; k; l ¼ 1; . . . ;n and l – j; i; j ¼ 1; . . . ;5:

ð3Þ

CiðAkÞ � CiðAlÞCjðAkÞ � CjðAlÞ

< 0;

k – l; k; l ¼ 1; . . . ;n and i – j; i; j ¼ 1; . . . ;5:ð4Þ

A decision-maker will often seek a solution that has a balancedimpact on each and all of the essential aspects of a rural commu-nity. The sustainable livelihoods pentagons discussed above areuseful to illustrate hypothetical cases of positive and negativeinterdependence between pairs of assets, Ci and Cj, if energy tech-nology Ak is chosen, rather than any other alternative. A positiveinterdependence between Ci and Cj takes place where two assetsincrease in the same direction when Al, instead of energy Ak, is ap-plied (Fig. 2(a)). In contrast, a negative interdependence between Ci

and Cj, results when Ci increases whilst Cj decreases when alterna-tive Al has been chosen instead of Ak (Fig. 2(b)).

The robustness mechanism assigns higher weight values to as-sets that could become positively impacted than to the remainingassets, so that a more positive solution is modelled. Eq. (5) reflectsthis relation of interdependence:

di;j ¼Xn�1

k¼1

Xn

l¼kþ1

qk;l; qk;l ¼1; if CiðAkÞ�CiðAlÞ

CjðAkÞ�CjðAlÞP 0;

0; otherwise;

( )

i ¼ 1; . . . ;4 and j ¼ iþ 1; . . . ;5:

ð5Þ

(a)

C1

C2

CjCi

C5

Ak Al Ideal pentagon

C

Fig. 2. Positive (a) and negative (b) interdep

where

½D�5�5 ¼

0 d12 d13 d14 d15

d21 0 d23 d24 d25

d31 d32 0 d34 d35

d41 d42 d43 0 d45

d51 d52 d53 d54 0

26666664

37777775

An Interdependence Decision Index is therefore proposed. In thematrix, each element dij represents the number of positive interde-pendent coincidences between assets i and j (for all i – j). There-fore, the resulting matrix D becomes symmetric and its diagonalelements will be zero; thus, it is enough to calculate the compo-nents contained in the upper triangle of D. Note that the index onlycalculates the number of positive interdependences, but not themagnitude of the interdependences themselves.

Following from the D matrix results, a weight value (wdi,i = 1, . . . ,5) is assigned to each asset, based upon the followingmathematical Eq. (6), where the denominator of Eq. (6) representsthe sum of the overall elements of D:

wdi ¼

P5j¼1ði–jÞ

dij

P5k¼1

P5j¼1

dkj

; ði ¼ 1; . . . ;5Þ: ð6Þ

In summary, as part of the robustness mechanism, the proposedtest of interdependence promotes the selection of solutions thathave positive effects on most assets. In this way, technologies thatmay have undesirable effects on the community can be avoided inthe selection process because their impacts are prevented fromgoing unnoticed—as the sensitivity approach makes their impactclearly visible.

5.2. Entropy tests to locate impacts

The entropy test is used to map the disorder, or dispersion, ofthe possible effects of various technologies on the community as-sets. The Entropy Index provides therefore a measure of the distri-bution of the scores of technology alternatives along the radiallines from the centre of the pentagon. The concept of entropy,which originates in thermodynamics, has been utilised withinthe multicriteria context to compute weight factors (Zeleny,1982; Jessop, 1999). Here, this index assigns more weight to assetswith large entropy than to assets that show smaller dispersion in

(b)

C1

C2

CjCi

5

Ak Al Ideal pentagon

endence between a pair of assets Ci, Cj.

C1

C2

C3C4

C5

A1 A2 A3 A4 Ideal pentagon

C1 has Small Entropy C2 has Large Entropy

Fig. 3. Small and large entropy of energy impact on five assets.


the alternatives’ scores (Fig. 3, see assets C1 and C2). That is, highervalues will be allocated to resources where the degree of theimpact depends strongly on the type of technology. The possiblelosses in terms of the pre-established goals could be larger in thecase of wider dispersion of the impact than in the case of narrower,more concentrated dispersion. Eq. (7) represents the entropy indexsuggested for this robustness analysis

Ej ¼ 1þ 1LnðnÞ

Xn

i¼1

C�j ðAiÞ � Ln C�j ðAiÞ� �h i

C�j ðAiÞ ¼CjðAiÞPn

k¼1CjðAkÞ; Ej 2 ½0;1�; ðj ¼ 1; . . . ;5Þ:

ð7Þ

In the case when all alternatives have the same value, Ej = 0,there is no entropy among the alternatives. When all the alterna-tives are uniformly distributed along an asset’s axis, however, Ej

obtains its maximum value, but this will always be Ej < 1.Then, the weight value proposed for each community’s asset

(wej j = 1, . . . ,5), based upon Ej, is calculated as follows:

wej ¼EjP5l¼1El

ðj ¼ 1; . . . ;5Þ: ð8Þ

Entropy analysis therefore provides a mechanism with which to as-sess the impacts of various technologies from an additional andhelpful perspective.

5.3. Avoiding unsuitable solutions

The approach seeks to avoid unsuitable and unsatisfactoryenergy solutions Eq. (9). The proposed mechanism aims to searchfor energy options that, at least, achieve a minimum aspiration le-vel (b) that is acceptable for the community regarding their expec-tations of improvement over the five SL assets. The minimumaspiration level (b) that would be acceptable to the community isinferred through case-study surveys. Thus, in the case where alarge number of energy options do not meet the registered aspira-tion b in relation to a particular asset, then the correspondingweight for that asset would be increased in order to favour thefew well-scored alternative solutions within that asset. As a conse-quence, larger weights are allocated to assets with lower chancesof meeting appropriate levels of aspiration (i.e., more concernand emphasis is placed on assets that have greater chances ofobtaining unsuccessful results). For instance, if an asset has threeout of its four options scoring below the threshold b (e.g. 50%), thisasset would be allocated a larger weight value than an asset thathas three out of its four options scoring above b. The weight valuesthat reflect the condition of unsuccessful alternatives for each asset(wsj j = 1, . . . ,5) are calculated as follows:

pj ¼Pn

i¼1ui

n; ui ¼

b� CjðAiÞ if CjðAiÞ < b

0 otherwise

�

wsj ¼pjP5l¼1pl

; ð1 6 j 6 5Þ:ð9Þ

where Cj(Ai) < b indicates that the performance of option Ai withrespect to asset Cj is lower than the minimum level of aspirationb expected for the community. This set of weightings aims to avoidsolutions that may not sufficiently contribute to the sustainabilityor efficiency of the technologies.

6. A case study for testing the proposed methodology

The proposed methodology has been tested through an applica-tion in the remote province of Jambaló, Department of Cauca,southwest Colombia, in 2004. The Jambaló study area comprisesthree adjacent small rural communities, i.e., La Esperanza, La Mar-queza and Loma Larga. The study area is no larger than 7 km2, withabout 370 inhabitants, mostly indigenous native descendants fromthe Páez ethnic group. Jambaló has deficient physical infrastruc-ture and is located distantly from the national grid. A structuredhousehold survey was applied to Jambaló to collect informationon energy development and potential, as well as on populationpriorities, needs and demands. Secondary sources were also con-sulted on climatology, topography and other specialities able toprovide information related to the study area.

The average income in the region was less than a dollar per day(i.e., below the international poverty line index). Traditional cattlehusbandry and agriculture — mainly sugar cane — was practised by85% of the population. The level of education in the region waslow: only 59% of the population had attended elementary school,5% had been through technical training, and as many as 36% wereilliterate. Access to basic services such as clean water, health,communication, and passable roads was very limited. Residentsaccessed water manually by pumping it from underground andstoring it in tanks. The nearest centre providing basic health assis-tance was located 20 km away, in the town of Jambaló.

Electricity was provided by a 10 kW capacity diesel plant, whichsupplied an equivalent of only 21% of the local demand, well belowthe population’s peak energy demand, which is estimated at47 kW, considering that energy consumption per inhabitant is389 kWh/yr, including some economic activities. Because of thefact that the geographical inaccessibility of Jambaló made fuelsexpensive (and consequently there have been long periods ofpower shortage when no fuel could be bought), residents couldnot afford to improve the existing energy supply. Intervieweespreferred an energy supply from renewable sources. The Jambalóregion is located in a high mountain inter-Andes rain forest zonethat has abundant natural resources useful for energy production,i.e., water, biomass and solar radiation.

The main demand in Jambaló is for domestic and public light-ing, followed by the mechanisation of sugar cane production (har-vesting and processing). The survey indicates that electricity isneeded to facilitate the irrigation of crops and to power machineryto extract liquid from sugar cane. Additional reported priorities arethe provision of energy for health care, followed by commercialactivities and, finally, for supporting education.

The average rainfall in the region is about 1800 mm per yearwhich, combined with a mountainous topography, provides a suit-able setup for a hydroelectric power plant. A further energy sourcein the region is sunlight; but due to cloudiness, only moderaterates are registered: about 3–4 kWh/m2/day may be reached. Final-ly, the presence of organic waste from the local sugar cane industryrepresents an important potential source for energy generationthrough a biomass power plant. Wind power does not represent

Table 1Energy alternatives available for Jambaló, Colombia, 2004.

Energy alternatives Energy source Efficiency[%]

Supplied capacity[kW]

Operation & maintenance costs[%]

Initial costs [USD/kW]

Total cost[USD]

Current diesel Diesel 70.0 10 10 0 0Micro hydro Water 80.0 47 2 to 5 1300 61,100Solar photovoltaic Sun 30.0 47 10 4250 199,750Biomass plant Sugarcane waste 35.0 47 10 900 42,300Current diesel + biomass

plantaDiesel + sugarcaneWaste

45.0 10 + 37 10 0 + 900 33,300

Current diesel + solar PVa Diesel + sun 45.0 10 + 37 10 0 + 4250 157,250Current diesel + micro

hydroaDiesel + water 75.0 10 + 37 10 (diesel) + 5% (micro-hydro) 0 + 1300 48,100

a Hybrid energy alternatives are composed of the current diesel plant, which has 10 kW capacity, plus other energy technologies, which would have the rest of the capacityto cover the total energy demand of the community, in this case 37 kW.

Table 2Current state of assets in Jambaló with a 10 kW Diesel Plant, Colombia, 2004.

Energy technology Estimated asset values

Physical Financial Natural Social Human

Current Diesel plant10 kW

0.18 0.33 0.66 0.35 0.25

Table 3The future energy impact of various technology options, Jambaló, Colombia, 2004.

Energy technology Expected asset values

Physical Financial Natural Social Human

Micro hydro 0.48 0.58 0.48 0.54 0.30Solar photovoltaic 0.20 0.33 0.65 0.43 0.48Biomass 0.71 0.67 0.58 0.52 0.49Current diesel + biomass 0.48 0.67 0.46 0.57 0.48Current diesel + solar PV 0.33 0.33 0.46 0.42 0.47Current diesel + micro

hydro0.66 0.58 0.44 0.67 0.32

Ideal pentagon 1 1 1 1 1


a viable source of energy generation, due to low wind speeds in theregion (about 2 m/s), and the fact that there are significant periodsof around 8–11 weeks of negligible wind.

6.1. Energy options modelled for Jambaló

The multicriteria approach and its robustness analysis wereused to calculate how well a particular energy solution would per-form in this case. The essential technical features to be consideredin the selection process are: required energy generation source,technology load efficiency, and demanded power capacity andcosts (see Table 1).

The modelled alternatives are the outcomes of an initial techni-cal and financial analysis to determine if a particular technologywould be feasible within the financial and technical capabilitiesof the community (Table 1). For example, for Jambaló, the total costof a micro-hydro solution would be relatively cheap (US$ 61,100) ifcompared with solar photovoltaic or combined diesel and solar(US$ 199,750 and US$ 157,250 respectively) but slightly moreexpensive than the rest (i.e., biomass, diesel and biomass, and die-sel and micro-hydro). While micro-hydro and diesel, or the twocombined, are the two most efficient options, with 80%, 70% and75% efficiency respectively, the other technologies only showabout half of those values. This analysis can also show that alterna-tives such as inter-connection to the national grid and the expansionof the current diesel plant are too costly, while wind power is consid-ered technically unfeasible, and therefore were not incorporated inthe table. The multicriteria analysis also provides detailed informa-tion to the decision-makers on the sustainability of the energyalternatives in human, social and environmental terms.

To assess the energy alternatives in relation to the five SL assets,Eq. (1) utilizes primary data from the household survey. The cur-rent state of assets in Jambaló, with the only source of energy beingits existing diesel generator, shows that the largest asset in theregion is its natural resources (0.66) and the smallest is physical(e.g., local infrastructure, 0.18) (Table 2). The expected future im-pact, in Jambaló, of an energy technology options (Ai, i = 1, . . . ,n)on each of the five assets Cj(Ai), j = 1, . . . ,5, using Eq. (1), shows that,e.g., biomass and then the combined solution of diesel and micro-hydro will have the larger impact on both the physical and the

financial assets of the community (with 0.71 and 0.66, and 0.67and 0.58 respectively) (Table 3). Also, a micro-hydro plant on itsown would result in a significant impact (e.g., 0.48 for naturalresources and 0.54 for social assets). Hence, Tables 2 and 3 com-prise the data that is required to perform the calculations of theCompromise Programming multicriteria method expressed in Eq.(2); i.e., Tables 2 and 3 form the decision matrix [C(A)]nx5 of themulticriteria problem.

The ‘‘Current Diesel + Solar PV’’ yields lower values than the ‘‘Bio-mass plant’’, over all assets (becoming a ‘‘dominated’’ option), andwould thus be discarded (Belton and Stewart, 2002). This leavessix remaining technology alternatives for analysis. The lower thevalue of an asset, the poorer the community and vice versa, i.e., val-ues close to one indicate a most developed condition with respectto a specific asset.

The improvement expected of an asset j as a result of imple-menting a particular energy option i, is the outcome of Cj (Ai) � C-j(A1), which indicates the difference between (a) the extent that anasset was available, and (b) that it could be available as a result ofusing additional energy services (A1 represents the current dieselplant).

The impact of the different energy alternatives on the five SL as-sets is presents in Fig. 4. This enables decision-makers to visualisethe multiple trade-offs between assets when considering adoptingone alternative over another. The multicriteria and robustnessapproach aims to assist decision-makers to promote sustainablelivelihoods and appropriate long-lasting energy solutions.

The values established by the decision-maker regarding theweight factors for the five assets (WDMs), after considering thecommunity’s priorities, were: Physical Resource, 0.30; FinancialResource, 0.25; Natural Resource, 0.10; Social Resource, 0.15; andHuman Resource, 0.20. Table 4 shows the technology rankingsbased on the distances (Dp) obtained when applying the Compro-mise Programming model, Eq. (2), with these weights and p = 2.

0.00

0.20

0.40

0.60

0.80

1.00Physical

Financial

NaturalSocial

Human

Current Diesel Micro Hydro Solar PhotovoltaicBiomass Current Diesel + Micro Hydro Current Diesel + BiomassIdeal pentagon

Fig. 4. The impact of energy alternatives on five assets, Jambaló, 2004.

Table 4Scores of the most appropriate technology options, Jambaló, Colombia, 2004.

Ranking Energy alternatives Compromiseprogramming (D2)

Scores ofoptions

1 Biomass 0.18 1.002 Current diesel + micro

hydro0.21 0.82

3 Currentdiesel + biomass

0.22 0.76

4 Micro hydro 0.25 0.595 Solar photovoltaic 0.32 0.186 Current diesel 0.35 0.00

Table 5Robustness analysis weight factors.

Assetsnweight sets WDMs Wd We Ws

Physical 0.30 0.24 0.51 0.27Financial 0.25 0.25 0.18 0.15Human 0.20 0.22 0.15 0.32Social 0.15 0.19 0.09 0.15Natural 0.10 0.10 0.06 0.11Sum 1.00 1.00 1.00 1.00


Although the model calculates distances (D2) between pentagonsand suggests the shortest as the most appropriate one, an addi-tional score is introduced by re-scaling the distances in order toprovide an alternative way of representing the result (Score ofoptions); where the shortest distance equal to 1.00 and the largestequal to 0.0.

The highest score indicates that a Biomass plant would providethe most suitable solution. The higher the score, the more likely atechnology option will promote sustainable development in acommunity. As results depend greatly on weights, further testingthrough the innovative robustness analysis proposed here is desir-able (as discussed previously).

6.2. Robustness analysis of technology selection for Jambaló

Three sets of weight factors, in addition to the community’sweight values, have been used for robustness analysis in this paper.The results can then be applied in the selection of a final outcomeby either giving support to the initially chosen option or bypresenting an alternative from among the portfolio of options.The factors used for this analysis were:

� Community’s initial weight values (WDMs).� Interdependence factor (wd).� Entropy factor (we).� Unsuitable solutions (ws).

Robustness analysis was conducted by running our CP set-upunder the new sets of weights (i.e., wd, we, ws). The results thatemerged have been contrasted with the results obtained usingthe initial weights. Robustness analysis identifies whether the en-ergy technology selected at the outset may change the initial rank-ing. This constitutes a sensitivity analysis of the initial solution.

Results of four different preference structures, regarding the rel-ative importance of the five community assets, are presented in Ta-ble 5. For instance, using the WDMs factor, the decision-makersvalued physical assets as the most important, as it takes by farthe highest value (0.30), while the least important asset wasnatural resources (0.10). In the case of wd, this indicates that thenatural asset is the most contentious one (0.10), as interventionswill inevitably damage the natural environment. For we, the phys-ical asset is the one with the largest dispersion, but with a rela-tively small impact (0.06); see Fig. 4.

To determine unsuitable solutions (ws), a minimum level ofaspiration b was set at 0.6, for all assets. This means that no lessthan 60% is the desired level to be achieved on all asset levels. Inthis case, ws indicates that the physical (0.27) and human (0.32)assets are the ones with more potentially unsuitable energy solu-tions (i.e. those that lie below the established threshold). As aresult, ws suggests allocating the highest weight factors to theseassets.

The resulting index relating to the entropy weight factor was:

The resulting Matrix D relating to interdependence factor was:

The resulting index relating to avoiding unsuitable solutions was:

The outcomes of this analysis represent the scores obtained byeach energy alternative after applying CP under a different set ofweights WDMs, wd, we and ws (see Table 6). The results indicatethat the Biomass option continues to perform best. In addition,results also suggest that the Current Diesel + Biomass hybrid optionis less robust than the Current Diesel + Micro Hydro energy option,under different preference structures. These results not only enableus to find robust energy alternatives, but also to uncover energyalternatives, which might outperform others under structures ofpreference different from the one initially suggested by the deci-sion-makers.

Comparison of distances between alternatives is crucial, espe-cially in cases where differences between the costs of the twofirst-ranked alternatives are high (i.e., the second ranked alterna-tive is cheaper than the first one), but the actual improvement

Table 6CP scores of the energy alternatives based on the robustness analysis weight factors.

Alternativesnoutcomes WDMs Wd We Ws

Biomass 1.00 1.00 1.00 1.00Current diesel + micro hydro 0.80 0.79 0.87 0.66Current diesel + biomass 0.67 0.71 0.62 0.78Micro hydro 0.59 0.60 0.57 0.47Solar photovoltaic 0.08 0.10 0.35 0.07Current diesel 0.00 0.00 0.00 0.00

0.00

0.20

0.40

0.60

0.80

1.00Physical

Financial

NaturalSocial

Human

Current Diesel Ideal pentagon Biomass

Fig. 5. SL pentagon’s improvement with the implementation of the biomass powerplant.


accruing to the five assets is small (e.g., the first alternative per-forms only slightly better than the second). In this particular case,it is argued that the cost difference between the first two alterna-tives is more important in comparison to the potential benefitsthat could be gained from the highest ranked alternative (i.e., theBiomass power plant). The robustness analysis indicates thereforethat the best energy alternative for Jambaló is the biomass powerplant, because it produces results near to one for all factors (seeFig. 4). The second best alternative is the Current Diesel + Micro Hy-dro plant, and then the Current Diesel + Biomass plant.

The improvement expected for Jambaló at the end of the life-span of the biomass energy solution (i.e., 15 years) is shown inthe pentagons (Fig. 5). The new energy option would enhancethe physical, financial and human assets; but little improvementwould be experienced for social assets; and finally, little loss ornegative impact on the natural assets would be expected.

7. Conclusions

A multicriteria decision-support model for selecting energysupply alternatives for isolated rural areas has been formulated.This approach draws on a sustainable livelihoods framework toenable the decision-making process to focus on achieving povertyreduction by means of sustainable development.

The sustainable livelihoods framework has provided importantinsight into development issues. The broader scope of the frame-work helps increase the understanding of the effects of electrifica-tion on the major community assets, and the capabilities generatedin terms of human and social well being.

Furthermore, a robustness analysis that selects not only sus-tainable, but also robust energy technology solutions for the poorhas been developed. Three additional sensitivity tests have beenproposed to recalculate the weight factors necessary for selectingthe energy technologies that most, and best, impact the sustainable

livelihood assets: i) interdependence between assets, ii) entropy ofimpacts, and iii) avoidance of unsatisfactory solutions. Robustnessanalysis is considered a central part of the approach as it aids in therefinement and the assurance of success of the decisions that aremade for a community.

This robustness analysis approach differs from others (e.g.Bertsch et al., 2007; Roy, 2010) as it is based on the context ofthe problem, particularly on the meaning of the five SL assetsand the potential configurations of the problem’s pentagons. It pro-poses variations of weight factors that have a particular meaningthat is context-related to the problem situation. Most otherapproaches, however, follow deterministic or stochastic mathe-matical procedures in proposing systematic variations over keyparameters of the models (e.g. the weight factors), which haveno meaning for the context and content of the problem situation(e.g. Tervonen et al., 2009).

A case study in a rural community in Colombia has been used totest the approach described in this paper. This application illus-trates that robustness analysis has the ability to show when an en-ergy option located below an alternative in a CompromiseProgramming model ranking can be considered as being as goodas the first option, rather than becoming a second choice.

Perhaps the main strength of this approach is that it enriches atheoretical concept such as sustainable livelihoods through amathematical model for the purposes of access to energy. Thisnot only suggests a novel way to operationalize the SL framework,but also sheds light on how to bridge the gap between decisionsupport tools and the latest developments in sustainability (Whiteand Lee, 2009).

This couples the intention of reducing poverty with the consid-eration of multiple factors rather than just technical and economicones. It takes into consideration the financial, infrastructural,natural, social and human dimensions of energy development.For decision-makers, the approach presented in this article istransparent, particularly with respect to the assessment of the im-pact of energy technologies on a community’s livelihoods.

There remains the potential for extending the approach to otherknowledge areas such as: other infrastructure-related issues,broader sustainability and poverty concerns, and more specificallyabout managing multiple decision-makers. In this last regard,research has shown that when different decision-makers are in-volved they not only differ in terms of value functions or weightfactors, but also in terms of problem structure and definition (e.g.what should be the problem focus, criteria and options to con-sider?) (Mingers and Rosenhead, 2004; Kasanen et al., 2000). Inthis sense, scholars have proposed to combine multicriteria ap-proaches with more flexible approaches, like problem structuringmethods (PSMs), to assist decision-makers make sense of their‘‘messy’’ situations (Belton et al., 1997; Petkov et al., 2007). Hence,the approaches proposed in this paper could be combined withPSMs in order to provide further assistance during the decision-making process (Howick and Ackermann, 2011). This could beoperationalized by dividing the process into two phases (Kaner,2007): a divergent phase where PSMs are employed to focus onproblem structuring and definition, and a convergent phase wherethe multicriteria approach and its robustness analysis are used toscore options and assess weight factors.

Acknowledgements

The authors especially thank the Department for InternationalDevelopment UK (DfID) for the award to the RESURL project(Renewable Energy for Sustainable Rural Livelihoods – KaR 8010)2001–2006 under which the present study was conducted. Specialthanks are also extended to the Colombian Research Council, COL-CIENCIAS, for financial support. The authors appreciate the


valuable comments of reviewers including editorial assistance viahttp://dr-paul-g-ellis.com.

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A multicriteria approach to sustainable energy supply for the rural poor

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