use of multi-criteria decision analysis to explore alternative domestic energy and electricity...

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Energy 35 (2010) 518–528

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Energy

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Use of multi-criteria decision analysis to explore alternative domestic energyand electricity policy scenarios in an Irish city-regionq

David Browne a,*, Bernadette O’Regan b,1, Richard Moles c,2

a Department of Civil, Structural and Environmental Engineering, Trinity College, Dublin (TCD), Dublin 2, Irelandb Centre for Environmental Research (CER), Foundation Building, University of Limerick, Castletroy, Irelandc Chemical and Environmental Sciences (CES) Department, University of Limerick, Castletroy, Ireland

a r t i c l e i n f o

Article history:Received 26 January 2009Received in revised form12 October 2009Accepted 14 October 2009Available online 29 December 2009

Keywords:Energy policyMulti-criteria decision analysis (MCDA)Scenario buildingEcological footprinting

q Formal Error Analysis: The results in this paper repotential scenarios based on policy targets and have bdata and indicator trends. The approach taken in thitational analysis application.

* Corresponding author. Tel.: þ353 1896319; fax: þE-mail addresses: [email protected], davidbrown

[email protected] (B. O’Regan), Richard.Moles1 Tel.: þ353 61202552; fax: þ353 61202568.2 Tel.: þ353 61202817; fax: þ353 61202568.

0360-5442/$ – see front matter � 2009 Elsevier Ltd.doi:10.1016/j.energy.2009.10.020

a b s t r a c t

In this paper, multi-criteria decision analysis (MCDA) was used to assess 6 policy measures or scenariosrelating to residential heating energy and domestic electricity consumption, using an Irish city-region ascase study. The analysis was undertaken using a modified version of MCDA based on the NAIADE (NovelApproach to Imprecise Assessment and Decision Environments) software and involved a decision outputbased on a mix of qualitative and quantitative assessment, which offered a ranking of options. It wasconcluded that Scenario 2, which proposes reducing energy and electricity consumption, was the mostpreferable option and Scenario 3, which proposes increasing the contribution of wood waste, was theleast preferable option. This suggests that absolute reduction and demand management should be pri-oritised over fuel substitution or renewable energy technologies.

MCDA was also compared with ecological footprint (EF) analysis for the same set of scenarios and itwas found that both metrics show that Scenario 2 is preferable. However, MCDA shows that Scenario 3 isthe least preferable scenario, whereas EF analysis suggests that Scenario 4 is, i.e. increased contributionof short rotation coppice (SRC). This suggests that a mix of assessment tools/indicators should be usedwhen attempting to identify the most justifiable policy options as different indicators reflect differentpolicy aspects.

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1. Introduction

Current energy policy in Ireland is set out in the 2007 WhitePaper Delivering a Sustainable Energy Future for Ireland [1], whichwas prepared following public consultation on the 2006 GreenPaper Towards a Sustainable Energy Future for Ireland [2]. Thescenarios adopted in this analysis, which are based on reducingenergy/electricity consumption and developing renewable fuelsand technologies, are in accordance with the Strategic Goals set outin the 2007 White Paper.

These Strategic Goals include inter alia: (i) ensuring security ofsupply and diversity of fuels; (ii) being prepared for energy supply

present an ex-ante analysis ofeen validated against real-lifes paper represents a compu-

353 [email protected] (D. Browne),

@ul.ie (R. Moles).

All rights reserved.

disruptions and ensuring market resilience through strategicreserves; (iii) addressing climate change by reducing emissions; (iv)accelerating the growth of renewable energy sources; (v) max-imising energy efficiency; and (vi) delivering structural changes tothe energy and electricity markets to ensure competitiveness andconsumer choice [1].

Renewable energy targets for the EU were outlined in the 1997White Paper Energy for the Future: Renewable Sources of Energy – AWhite Paper for a Community Strategy and Action Plan,3 which seta target of doubling the contribution of renewable energy to the EUprimary energy supply or Total Primary Energy Requirement (TPER)from 6% to 12% by 2010. In the 2008 EU Energy and Climate ChangePackage, which included a proposal for a Directive on the Promotionof the Use of Energy from Renewable Sources,4 differentiatedrenewable energy targets were set for EU Member States, includinga 16% target for Ireland for share of energy from renewable sourcesin final consumption of energy by 2020, compared with a 2005

3 http://ec.europa.eu/energy/library/599fi_en.pdf, last referenced October 2009.4 http://eur-lex.europa.eu/LexUriServ/LexUriServ.do?uri¼COM:2008:0019:FIN:

EN:PDF, last referenced October 2009.

mailto:[email protected]




http://ec.europa.eu/energy/library/599fi_en.pdf

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D. Browne et al. / Energy 35 (2010) 518–528 519

baseline of 3.1%.5 This may be compared with an overall 20% targetfor the EU by 2020 and a 10% binding minimum target for biofuelsin transport.

Renewable electricity targets for the EU were set out in Directive2001/77/EC,6 which provided indicative targets for each MemberState for the contribution of renewable electricity to gross elec-tricity consumption by 2010, with Ireland being given a target ofa 13.2% share of gross electricity consumption. These targets wereconsistent with the indicative target contribution of 22.1% ofrenewable electricity to gross EU electricity consumption by 2010.

The 2006 Irish Green Paper set a revised target of 15% of electricityconsumption to be achieved from renewable sources by 2010 anda further target of 30% by 2020, subject to technical considerations.The 2007 White Paper subsequently sets a target of 33% of electricityconsumption from renewable energy sources by 2020 [1,2].

The aim of this paper is to use multi-criteria decision analysis(MCDA) in order to assess the impact of residential heating energyand domestic electricity consumption by the residents of an Irishcity-region, using environmental and socio-economic criteria, and toanalyse a number of policy scenarios in order to determine how theyshould be prioritised. Its specific objectives are: (i) to identifypotential measures to reduce the environmental impact of energyand electricity consumption in the residential sector; (ii) to test theapplicability of MCDA in energy scenario analysis for an Irish case-study; and (iii) to assess the similarities/differences between MCDAand a single biophysical measure, namely ecological footprint (EF)analysis.

The analysis in this paper was undertaken using the Limerickcity-region, which is the major urban centre in the Mid-West regionin the Republic of Ireland, as a case study [3]. This was an extensionof previous work undertaken on ecological footprinting of energyand electricity policy scenarios for the same study area, whichfound that the EF for domestic energy and electricity consumptionby Limerick residents increased by 7% from 0.125 global hectares(GHa) per capita in 1996 to 0.134 GHa per capita in 2002 [4].

The EF scenarios were based on an aggregate of actual landappropriation for energy infrastructure as well as the theoreticalland required to sequester greenhouse gas (GHG) emissions. Thisanalysis was undertaken as part of a wider sustainability appraisalof the Limerick city-region using various metrics and indicatorsrelating to material and product flows and embodied and directenergy consumption [3].

Scenarios are evaluated using a decision analysis framework,which ranks policy scenarios across a range of criteria, includingthe EF, other environmental impacts, security of supply, price to theconsumer and social benefits. The results are presented using theNAIADE (Novel Approach to Imprecise Assessment and DecisionEnvironments) software in the form of an impact matrix, whichillustrates preferential policy options where criteria are assessedaccording to an optimisation criterion and a decision output isproduced based on a pairwise comparison between alternatives.

The MCDA approach, as adopted in this paper, incorporates the EFas a criterion as well as other environmental impacts and socio-economic criteria. The outputs of the MCDA approach are thencompared with ecological footprinting as a tool to determinewhether these metrics adequately measure the impact of domesticenergy and electricity consumption and to compare analyticalresults. The objective is to determine whether the EF is the dominantparameter or whether other environmental or socio-economic

5 http://ec.europa.eu/energy/climate_actions/doc/2008_res_ia_en.pdf, last refer-enced October 2009.

6 http://eur-lex.europa.eu/pri/en/oj/dat/2001/l_283/l_28320011027en00330040.pdf, last referenced October 2009.

criteria are significant in determining the overall impact of a policyscenario. Sensitivity analysis is then undertaken by removing the EFcriterion and analysing the scenarios to see whether this is thedominant criterion. In addition, the ‘security of supply’ criterion isremoved so that inputs are solely quantitative in nature.

Section 2 of this paper introduces MCDA, the various methodsthat can be used, its applications in energy planning and policyanalysis and its strengths and weaknesses. Section 3 outlines theprocedural steps generally taken in MCDA, the specific methodo-logical approach that was adopted in this paper, the assumptionsregarding assessment and criteria and the NAIADE software thatwas used to generate outputs. Section 4 presents the results ofapplying MCDA to the selected scenarios. Finally, Section 5discusses the outcome of the MCDA, compares the results withthose generated from EF analysis alone as well as those scenariosthat are evaluated using sensitivity analysis and offers recom-mendations on the application of MCDA and other decision-aidingtechniques in energy policy analysis.

2. Multi-criteria decision analysis (MCDA)

Decision-aiding techniques guide the decision-making processby offering insights to stakeholders involved in developingscenarios and allowing for consensus to be developed. In addition,some decision-aiding techniques may be used to integrate qualita-tive and quantitative information into a single assessment or output.Such techniques may involve a given set of alternatives provided bythe decision-maker, a set of criteria for comparing the alternatives,the assigning of weights to criteria and a method for ranking thealternatives based on how well they satisfy the criteria [5,6].However, decision-aiding techniques and decision support meth-odologies (DSM) vary according to the qualitative and quantitativenature of data; the degree of complexity, uncertainty and fuzzinessembedded in the dataset; the use of weightings for criteria and therobustness and transparency of results [7].

Decision-aiding techniques include various methods such as (i)general utility analysis, for example the Analytical HierarchyProcess (AHP), which uses pairwise comparisons to assess decision-makers’ preferences; (ii) outranking methodologies such as Pref-erence Ranking Organization Method for Enrichment Evaluations(PROMETHEE) and Elimination Et Choix Traduisant la Realite(ELECTRE); and (iii) social multi-criteria evaluation (SMCE) tech-niques such as Novel Approach for Imprecise Assessment andDecision Evaluations (NAIADE) [6,8].

Multi-criteria decision analysis (MCDA) is used for decision-making in environmental policy due to the complexity of issues andthe inadequacies of conventional tools such as cost-benefit analysis(CBA), cost-effectiveness analysis (CEA) and environmental impactassessment (EIA) for capturing the full range of impacts of a policyor capital project. For example, some measures only analyse a singleparameter or focus on quantifiable data at the expense of qualita-tive assessment or less tangible impacts [9].

It may be argued that conventional reductionist techniques, suchas biophysical or chrematistic measures, often lead to less thanoptimal decisions by considering a set of objectives and criteria thatmay be in conflict, multi-dimensional, incomparable or incom-mensurable [9]. In MCDA, the decision process is as important as thefinal solution and involves both substantive and procedural reality.The principle aim is not to discover a solution but to construct orcreate something, which is likely to help ‘‘an actor taking part ina decision process either to shape, and/or to argue and/or to transformhis preferences, or to make a decision in conformity with his goals’’ [9].

The strengths of MCDA, compared with reductionist techniquessuch as CBA or CEA, include: (i) it is useful for resolving conflictinginterests; (ii) it promotes public participation and bottom–up

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D. Browne et al. / Energy 35 (2010) 518–528520

democracy through transparent decision-making; (iii) it can be usedfor heuristic purposes, where the objective is process-orientedrather than results-oriented; (iv) it provides a single decision output,which aggregates individual impacts; (v) it facilitates multi-disci-plinarity and can be adapted to include biophysical, socio-economicand political criteria; (vi) it can be modified to weight criteria withstakeholder input, depending on the software or techniques used;(vii) it allows for analysis of incommensurable and uncertain criteria;(viii) it can be used to complement reductionist monetary orbiophysical measures in a more holistic approach; (ix) it allows forcriteria to be included which are difficult to monetise or measure;and (x) it can aggregate both qualitative and quantitative informa-tion [9,10].

However, it may be dependent on subjective or value-ladenjudgement, particularly in qualitative analysis, and it may be difficultto quantify environmental impacts or changes in social cohesion[9,10].

In a review of multi-criteria decision-making techniques, asapplied to sustainable energy management, it was found that resultsare often validated with multiple methods and fuzzy methods may beused to tackle uncertainties in the data [11]. NAIADE was used in thispaper to test the applicability of this tool to energy policy scenarios.The application of MCDA methods to energy planning problems hasbeen divided into three main groups: (i) value measurement models;(ii) goal, aspiration and reference level models; and (iii) outrankingmodels [12].

Methods from all of these groups have been applied to energyplanning problems, particularly in the evaluation of alternativeelectricity supply strategies, and each of the methods has itsadvantages and drawbacks. However, it has been concluded that noone method is better suited than the others for energy planningproblems and a possible alternative might be to apply more thanone method, either in combination to make use of the strengths ofboth methods, or in parallel to get a broader decision basis for thedecision-maker [12].

MCDA will not facilitate the discovery of an ideal or optimumsolution but allows for the creation and emergence of consensusaround justified solutions or strategies [13]. Furthermore, energyplanning involves many value judgments regarding technical,socio-economic and environmental issues and, therefore, reachingclear and unambiguous solutions may be difficult [13].

It is proposed that future work could test this hypothesis byincluding wider stakeholder inputs, assigning weightings to criteriausing other DSM approaches and adjusting scenarios. A multi-criteria approach based on NAIADE has also been used to select theoptimal scenarios for natural gas energy systems and it was foundthat a combined gas and steam turbine cycle is the optimal solutionfor each criteria group for all analysed scenarios [14].

MCDA has also been used to evaluate small-scale energy appli-cations in Yorkshire in the UK and it was found that small-scaleschemes are the most socially, economically and environmentallyeffective, despite large-scale schemes being more financially viable[14]. It was concluded that the selection of criteria on which thealternatives are assessed and the weights for each criterion areassigned is of critical importance and, therefore, it is important toinclude the relevant stakeholders to elicit such information [15].

That particular study used the MACBETH (Measuring Attractive-ness by a Categorical Based Evaluation Technique in Multi-criteriaDecision Aid) method, which involves a series of pairwise compari-sons, where the decision-maker is asked to specify the difference inattractiveness between all of the alternatives, and was chosen due tothe ease with which the method can handle values that are difficultto quantify as it allows for qualitative judgments [15].

MCDA has also been used to evaluate several combined heat andpower (CHP) system options with respect to end user requirements

and it was concluded that the combination weighting methodshould be used in the selection of CHP schemes. This methodincludes multiplication synthesis and additive synthesis techniquessuch as optimal weighting based on sum of squares, optimalweighting based on minimum bias and optimal weighting based onrelational coefficient of gradation [16].

Other examples of MCDA in energy policy decision-makinginclude the development of a framework for the assessment ofbioenergy systems to test their economic viability, environmentalperformance and social acceptability [17]; processing of integratedassessment information for climate change policy [18]; measuringthe impact of pollutant reduction and energy system developmentstrategies [19]; and measuring the impact of different scenarios forpower generation in Greece [20].

Multi-attribute decision-making has been used for the selectionof a suitable electricity generation alternative for Turkey, wherePROMETHEE was used to evaluate renewable electricity alternatives[21]. Fuzzy multi-criteria decision-making methodologies have alsobeen used for the selection of renewable energy alternatives, wherefuzzy axiomatic design was applied to the selection of the renewableenergy alternatives in Turkey [22]. MCDA tools, including SuperDecisions (based on AHP), DecideIT (based on the DELTA method),Decision Lab (based on PROMETHEE II) and NAIADE, were used toevaluate the sustainability of bioenergy systems in Uganda [23].

It may be argued that conventional reductionist techniques,such as biophysical or chrematistic measures, often lead to less thanoptimal decisions by considering a set of objectives and criteria thatmay be in conflict, multi-dimensional, incomparable or incom-mensurable [9]. In MCDA, the decision process is as important asthe final solution and involves both substantive and proceduralreality. The principle aim is not to discover a solution but toconstruct or create something, which is likely to help ‘‘an actortaking part in a decision process either to shape, and/or to argue and/or to transform his preferences, or to make a decision in conformitywith his goals’’ [9].

In this paper, the NAIADE software was used in order to evaluatedifferent environmental, economic and social criteria in a multi-disciplinary manner. Potential future work could involve comparingthe results of NAIADE with other MCDA or DSM techniques.

3. Methodological approach

The procedural steps for MCDA may include: (i) establishing thedecision context or defining the nature of the decision; (ii) defini-tion and structuring of the problem or hypothesis; (iii) identifica-tion of possible scenarios; (iv) definition of a set of evaluationcriteria; (v) construction of a criterion impact or payoff matrix; (vi)choice between discrete and continuous methods or quantitativeand qualitative data; (vii) qualitative assessment and comparison ofcriteria using outranking relations; (viii) identification of the pref-erence system of the decision-maker and actors; (ix) evaluation ofresults and (x) sensitivity analysis [9,24].

A modified approach to this framework was used. The approachtaken did not seek to develop weightings for policy options,although this may be attempted as part of future work through theuse of alternative MCDA techniques. Weightings were not devel-oped for the various criteria due to project constraints and in orderto avoid subjective value judgments in the absence of externalstakeholder input. Furthermore, weightings are arbitrary in theabsence of a common parameter and may be difficult to introducewhere there is a mix of qualitative and quantitative assessment.

One way of resolving this would be to measure impacts based ona common quantitative parameter and then use a multiplier toweight impacts based on an ordinal scale of preferences. Sensitivityanalysis was undertaken by removing certain criteria and re-


evaluating the decision output. In addition, the results werecompared with those obtained from EF analysis of the samescenarios across the system boundary.

Weightings can be used with other MCDA tools to evaluate thelevel of priority for particular criteria, if stakeholder input can beobjectively included. However, it may be difficult to assignweightings to discrete quantitative data, unless a common indi-cator can be developed, e.g. toxicity, monetary cost, societal impact,etc. Future work could involve developing a consensus onweightings by a group of stakeholders, policy-makers, technicalexperts and citizens who have access to relevant data and infor-mation on the likely quantitative impacts.

However, such a process is cumbersome and involves reasonedinputs by stakeholders, which may not be possible if data are notavailable or impacts are not transparent. It is important to strikea balance between expert-led assessment and participatoryinvolvement and potential future work could involve a more bottom–up assessment based on stakeholder input framed by adequateknowledge.

In this paper, although weightings were not deployed, qualitativeassessment of the security of supply criterion based on reasonedjudgment and available literature was undertaken by the authorsdue to project constraints. Some of the key questions that arise,when developing stakeholder consensus, are the number of partic-ipants, the composition and background of the discussion group, theweighting given to different disciplines, the requirement of multi-disciplinary plurality and the difficulty in developing commonnumeraires. The paper used quantitative assessment where datawere available and qualitative assessment where discrete data werenot available or quantitative assessment was not possible.

Alternative scenarios are assessed using NAIADE, which isa discrete multi-criteria valuation matrix. This allows for both qual-itative and quantitative assessment including discrete, stochastic(probability density function) or linguistic expressions (qualitativeevaluation) [9]. NAIADE allows for the ranking of alternativesaccording to a set of evaluation criteria or preferences through theuse of an impact matrix [25].

NAIADE was used to rank alternatives using certain key criteriain order to identify particular scenarios, which could be justified byreference to the key parameters. However, it does not produce an‘optimal’ scenario and its usefulness is defined by the selection ofscenarios, choice of criteria and inputs, including both qualitativeassessment and discrete quantitative data.

The purpose of NAIADE is not to produce a definitive ranking ofalternatives but to rationalize the problem and provide a frame-work for communication among stakeholders. Thus, it allowspolicy-makers to develop pairwise comparisons, which may reduce

Table 1Criteria for MCDA of domestic energy and electricity scenarios.

Criterion Unit Score type Optimizationcriterion

Ecologicalfootprint (EF)

Global hectares(GHa) per capita

Quantitative Minimise

Sulphurdioxide (SO2)

Tonnes per capita Quantitative Minimise

Nitrogenoxides (NOx)

Tonnes per capita Quantitative Minimise

Other greenhousegases (GWP)

Tonnes CO2-equivalentsper capita

Quantitative Minimise

Security of supply Percentage of imports Qualitative MinimiseEnergy costs

(2004 Prices)V per capita Quantitative Minimise

Jobs created Number of jobs percapita consumption

Quantitative Maximize

the degree of conflict between different social groups or stake-holders [7]. In this paper, NAIADE was used to identify the mostpreferable scenario using different criteria and assumptions andthe output is expressed using the impact matrix.

It was selected as an example of a transparent outrankingmethodology, which can facilitate both quantitative and qualitativeassessment, and it was decided that this was an appropriate meth-odology to use given the availability of data and lack of stakeholderinput. Future work could focus on developing scenarios with stake-holder input and testing assumptions using different MCDA tools.

Criteria were developed using a mix of quantitative data andsubjective qualitative assessment and were used by NAIADE to rankscenarios, in the absence of any explicit weightings being assigned tothe criteria. Future work could involve re-examining the scenariosusing alternative DSM techniques and assigning weightings to thecriteria as a result of community stakeholder input. This could bedone to test the results in this paper, which aim to reflect a simpletransparent approach to MCDA based on available information andverifiable data rather than value judgments and qualitative assess-ment undertaken by general stakeholders.

4. Application of MCDA to scenarios

The principal elements to this application of MCDA include: (i)the scenarios that were selected, including business as usual (BAU),reduction of electricity and electricity consumption and substitu-tion of renewable fuels or technologies such as wood, short rotationcoppice (SRC), municipal waste or heat pumps; (ii) the variouscriteria used to examine scenarios, which included environmentaland socio-economic parameters; and (iii) the evaluation matrix,which used to examine the ranking order of scenarios.

The scenarios that are analysed for 2010 include:

1. Business as Usual (BAU);2. Reduce energy and electricity consumption by 20% of 2002

Total Final Consumption (TFC) by 2010;3. Increase contribution of wood waste to 12% of direct energy

consumption and 15% of electricity consumption by 2010;4. Increase contribution of short rotation coppice (SRC) to 12% of

direct energy consumption and 15% of electricity consumptionby 2010;

5. Increase contribution of municipal waste combustion (MWC)to 12% of direct energy consumption and 15% of electricityconsumption by 2010; and

6. Increase contribution of heat pumps to 12% of direct energyconsumption and wind, photovoltaic, tidal and wave to 15% ofelectricity consumption by 2010.

These scenarios were based on assumptions regarding BAU,reducing consumption through demand management measures

Table 2Emission factors for electricity by source (kg/MWh) (Pers. Comm., SustainableEnergy Ireland (SEI), 2006).



Nitrousoxide (N2O)

Methane(CH4)

Coal 2.2 1.27 0.008 0.005Oil 5.14 0.58 0.007 0.011Natural gas 0 0 0.003 0.009Wood and straw 0.1 0.47 0.014 0.115Short rotation

coppice (SRC)0.17 0.36 0.007 0.005

Municipal wastecombustion (MWC)

0.56 0.54 0.014 0.022

Wind 0 0 0 0

Table 3Emission factors for heat by source (kg/MWh) (Pers. Comm., Sustainable EnergyIreland (SEI), 2006).



Nitrousoxide (N2O)

Methane(CH4)

Coal 2.34 0.72 0.011 0.05Oil 0.08 0.19 0.007 0.005Natural gas 0.001 0.37 0.004 1.3Wood and Straw 0.09 0.47 0.014 0.12Short rotation

coppice (SRC)0.17 0.36 0.007 0.005

Municipal wastecombustion (MWC)

0.56 0.54 0.014 0.022

Heat pump 1.27 0.65 0 0

Table 4Total Final Consumption (TFC) of energy share in Ireland, 1996 and 2002 (%).

1996 2002

Solid fuel and coal 9.2 4.4Heating, kerosene, gasoil and other petroleum products 59.7 65.3Natural gas and liquid petroleum gas 14.4 13.1Combustible renewables and waste 1.3 1.4Electricity 15.5 15.9


such as fiscal instruments, awareness or energy efficiency andsubstitution of renewable fuels and technologies. The renewabletechnologies examined here represent a broad cross-section oftechnology options but are by no means exhaustive or exclusive.The scenarios are based also on national targets set out under EUDirectives, as outlined in Section 1. Future work could involvevarying scenario parameters and testing the results.

Policy scenarios were analysed for the period to 2010 as they arebased on trends between 1996 and 2002 and this representsa reasonable time horizon for scenario building and forecasting.This trend was chosen as it represents the study period for the city-region under analysis as well as the availability of national and localdata from the Irish Central Statistics Office (CSO). This analysis wasundertaken prior to the availability of results from the 2006 IrishCensus.

In addition, 2010 represents the date for key targets under the1997 EU Energy White Paper and the 2001 Renewable ElectricityDirective. As part of future work, the actual trends in 2010 could becompared with projected scenarios as part of ex-post evaluation. Inaddition, longer-term trends to 2020 could be examined to includefurther scenarios on reduction in energy consumption and substi-tution of renewable energy technologies in the fuel or electricity mixagainst targets set in the 2008 EU Energy and Climate ChangePackage.7

A range of potential criteria were identified for measuring theimpact of energy consumption, including EF, localised air pollutants,other greenhouse gases, security of supply, energy costs toconsumers and job creation, as shown in Table 1. These criteriainclude both qualitative and quantitative measures and the objectiveof maximisation or minimisation depends on the particular criterion.This paper specifically focuses on residential heat energy and elec-tricity consumption as one aspect of sustainable consumption. Otherforms of energy consumption, e.g. transport and embodied energy inproduct consumption, have been analysed elsewhere [26,27].

Tables 2 and 3 show the emission factors for electricity andheating equipment, including those for sulphur dioxide (SO2),nitrogen oxides (NOx), nitrous oxide (N2O) and methane (CH4) perunit of energy, and are applied to the feedstock fuels, which are usedto generate electricity or for residential heating. Total finalconsumption (TFC) of energy for residential heating for Irish resi-dents was 2,190,000 tonnes of oil equivalent (TOE) or 0.6 TOE percapita in 1996 and 2,680,000 TOE or 0.68 TOE per capita in 2002 [28].

Using population proxies and an expenditure proxy of Limerickresident domestic energy expenditure to national expenditure, fromthe 1999 to 2000 National Household Budget Survey (NHBS), it wasestimated that consumption by Limerick residents was 39,334 TOEor 0.5 TOE per capita in 1996 and 48,980 TOE or 0.56 TOE per capita

7 http://ec.europa.eu/environment/climat/climate_action.htm, last referencedSeptember 2009.

in 2002. TOE were converted to megawatt-hours (MWh),8 as emis-sion factors were available in units of kg/MWh, and it was estimatedthat domestic TFC was 5.815 MWh per capita in 1996 and6.513 MWh per capita in 2002.

TFC of energy share in Ireland for both 1996 and 2002 is shownin Table 4. Projected share of domestic energy consumption in 2010is solid fuel and coal (1.5%), natural gas and liquid petroleum gas(LPG) (12.8%), heating, kerosene, gasoil and other petroleumproducts (68.8%), electricity (15.6%) and combustible renewablesand waste (1.3%), based on current trends and fuel share.

Percentage share in 2010 is estimated to be the ratio betweenthe ratio of projected fuel share and TFC based on business as usualtrends between 1996 and 2002 without any policy interventions.This implies that petroleum will have a far more dominant share in2010 and solid fuel and coal share will have fallen from 4.4% in 2002to 1.5% in 2010.

In Scenario 1, which is a BAU scenario, TFC for residential heatingfor Limerick residents is estimated to increase from 0.56 TOE percapita in 2002 to 0.66 TOE per capita in 2010, based on an extrap-olation of the trend between 1996 and 2002, i.e. TFC increased by12% from 0.5 TOE per capita in 1996 to 0.56 TOE per capita in 2002. Itis projected that the composition of TFC in 2010 will include0.01 TOE of solid fuel and coal, 0.08 TOE of natural gas and LPG,0.46 TOE of kerosene, gasoil and other petroleum products, 0.1 TOEof electricity and 0.01 TOE of combustible renewables and waste.

In Scenario 2, which assumes a 20% reduction from the 2002 TFCfor residential heating for Limerick residents, TFC is projected to be0.45 TOE per capita in 2010, i.e. 20% below the 2002 figure of0.56 TOE per capita. TFC for residential heating for Limerick resi-dents for Scenarios 3–6 is estimated to increase from 0.56 TOE percapita in 2002 to 0.66 TOE per capita in 2010. However, it isassumed that renewable energy represents 12% of direct energyconsumption and displaces heating oil and petroleum productconsumption. Thus, TFC includes a reduction from BAU to 0.38 TOEof kerosene, gasoil and other petroleum products and an increase to0.08 TOE of renewable energy.

Total electricity consumption by Irish residents was5,740,000 megawatt-hours (MWh) or 1.58 MWh per capita in 1996and 7,450,000 MWh or 1.9 MWh per capita in 2002 [28]. Usingpopulation proxies and an expenditure proxy of Limerick residentdomestic electricity expenditure to national expenditure, it wasestimated that consumption of electricity by Limerick residents fordomestic purposes was 103,088 MWh or 1.3 MWh per capita in1996 and 136,152 MWh or 1.57 MWh in 2002.

Projected electricity generation in 2010 is estimated to be coal(24.3%), oil (20.5%), natural gas (48.6%), combustible renewables(0.47%), solar, tide and wind (2.6%) and hydroelectricity (3.6%),based on an extrapolation of BAU trends between 1996 and 2002,and total electricity generated from renewable sources is estimatedto be 6.7% of the total. Total electricity consumption by Limerickresidents for domestic purposes was estimated to increase to2 MWh per capita by 2010 in Scenario 1.

8 1 tonne of oil equivalent (TOE)¼ 11.63 megawatt-hours (MWh).

http://ec.europa.eu/environment/climat/climate_action.htm

Table 6Domestic energy and electricity emission data (tonnes per capita) – scenario 2.



Nitrous oxide(N2O)

Methane(CH4)

Solid fuel and coal 0.0001823 0.0000561 0.0000008571 0.000004Kerosene, gasoil

and otherpetroleumproducts

0.0002884 0.000685 0.00002524 0.000018

Natural gas 0.0000006745 0.00025 0.0000027 0.000877Electricity 0.000002024 0.000000922 0.00000000622 0.00000001Combustible

renewables andwaste

0.000039 0.00003768 0.000001 0.000001535

Total emissions percapita

0.000512 0.00103 0.00003 0.0009




Nitrousoxide (N2O)

Methane(CH4)

Solid fueland coal

0.000272 0.00008374 0.00000128 0.000005815

Kerosene,gasoil and otherpetroleumproducts

0.0004187 0.001 0.00003663 0.00002617

Natural gas 0.00000093 0.0003442 0.000003722 0.00121Electricity 0.000003186 0.000001457 0.00000001 0.0000000159Combustible

renewablesand waste

0.00005862 0.00005652 0.000001465 0.0000023

Total emissionsper capita

0.000753 0.00149 0.000043 0.00124




Nitrous oxide(N2O)

Methane(CH4)



0.000366 0.0008273 0.00003182 0.00002386


Fuel sources of electricity consumed by Limerick residents areprojected to include coal (0.486 MWh per capita), oil (0.41 MWhper capita), natural gas (0.972 MWh per capita), combustiblerenewables (0.0094 MWh per capita), solar, tide and wind(0.052 MWh per capita) and hydroelectricity (0.072 MWh percapita). In Scenario 2, total electricity consumption by Limerickresidents for domestic purposes was assumed to fall by 20% from2002 levels to 1.26 MWh per capita.

In Scenarios 3–6, it is projected that electricity consumed byLimerick residents is generated from coal (0.486 MWh per capita),oil (0.24 MWh per capita), natural gas (0.972 MWh per capita) andrenewable sources (0.3 MWh per capita). The EF was calculated forthe six scenarios in an earlier study [4]. This study evaluated the EFin terms of the theoretical land area required to sequester carbonemissions from energy and electricity consumption by residents ofthe Limerick city-region and to support energy infrastructure anddevelopment and developed the footprint as an aggregate of boththe actual land footprint and theoretical carbon footprint. The studyused the same study boundary and scenarios as those presented inthis paper, which facilitates cross-analysis.

The EF was also used to analyse the impact of potential scenariosand policies and results were compared with the BAU projection inorder to identify the optimal policy measure [4]. The results of thisanalysis are given in Table 14. Using EF analysis alone, it wasconcluded that Scenario 2, which proposes reducing energy andelectricity consumption, was the most preferable option andScenario 4, which proposes increasing contribution of SRC, was theleast preferable option [4].

Tables 5–10 show emissions of sulphur dioxide (SO2), nitrogenoxides (NOx), nitrous oxide (N2O) and methane (CH4) for each of the6 scenarios, which are calculated using TFC per capita for residen-tial energy and electricity for the various scenarios as well as theemission factors in Tables 2 and 3. Total emissions of SO2 and NOx

are given in Table 14. The Global Warming Potential (GWP) formethane and nitrous oxide was estimated for each scenario andthese aggregates are also given in Table 14. These estimates arebased on a GWP over a 100-y time horizon of 21 for CH4 and 310 forN2O9 and are calculated by adjusting emissions totals by multi-plying by the GWP factor and aggregating the total.

Security of supply was also evaluated for the 6 scenarios andwas based on assumptions about import dependency. Security ofsupply may generally be defined as ‘‘measures taken to reduce therisks of supply disruptions below a certain tolerable level as well asensuring that a supply of affordable energy is available to meet

9 http://www.epa.gov/nonco2/econ-inv/table.html, last referenced September2009.

demand’’ [29]. This implies that a secure energy system is resilientin the event of system shocks or perturbations and is less depen-dent on energy imports. This paper does not look at the geopoliticsof fossil fuel supply or the implications of contracted supply oravailability but assumes that fuel independence should be a keyobjective.

Import dependency in Ireland in 2003 was 87.1% for all fuels,including 66% for solid fuels, 96% for oil and 85% for gas. Importdependency is projected to be 88% in 2010 for the BAU scenario,taking into account further domestic extraction of natural gas andthe production of renewable fuels and electricity from indigenoussources [29]. Another important factor to be considered in the issueof security of supply is whether energy imports are sourced froma limited number of exporting regions or fuel supplies.

In this paper, it was assumed that the aggregate proportion ofimport dependency consists of an average mix of fossil fueldependency for Ireland, whereas scenarios based on renewablefuels assume that substitution of renewables reduces importdependency. However, it should be noted that not all renewablesare necessarily produced or generated domestically and similarthreats may exist for biomass resources, for example, particularlywhen global demand increases. In this paper, it was assumed thatall renewable fuels are sourced domestically.

A qualitative assessment of security of supply for the differentscenarios is shown in Table 14 and is based on the assumption thatcontinued high import dependency represents an undesirablescenario whereas reduction in energy consumption or highersubstitution of renewable energy and electricity represents rela-tively more favourable scenarios.

The NAIADE programme allows for qualitative assessment ofcriteria, ranging from ‘very bad’ to ‘very good’. Assuming highimport dependency is ‘very bad’, Scenario 1 is assessed using thiscriterion as it involves a BAU scenario with continued high importdependency. Scenario 2 is assessed as ‘bad’ as it has the same

Natural gas 0.00000093 0.0003442 0.000003722 0.00121Electricity 0.000002364 0.000001528 0.000000013 0.0000000487Wood waste 0.000083736 0.0004354 0.0000134 0.0001072Total emissions

per capita0.000725 0.00169 0.00005 0.00135

http://www.epa.gov/nonco2/econ-inv/table.html




Nitrousoxide (N2O)

Methane(CH4)



0.000366 0.0008273 0.00003182 0.00002386

Natural gas 0.00000093 0.0003442 0.000003722 0.00121Electricity 0.00000239 0.0000015 0.00000001084 0.0000000158Short rotation

coppice (SRC)0.0001574 0.000335 0.0000067 0.000005


0.0008 0.00159 0.0000435 0.00125




Nitrousoxide (N2O)

Methane(CH4)



0.000366 0.0008273 0.00003182 0.00002386

Natural gas 0.00000093 0.0003442 0.000003722 0.00121Electricity 0.00000234 0.00000139 0.00000000868 0.00000001414Heat pumps 0.001177 0.000608 - -Total emissions

per capita0.00182 0.00187 0.0000368 0.00124


percentage of imported fossil fuels but lower absolute imports dueto reduced energy demand and consumption.

Scenarios 3–6 are assessed as ‘more or less bad’, which impliescomparatively higher security of supply as they involve 12%substitution of renewable indigenous fuels for domestic energy and15% domestic electricity derived from renewable indigenous sour-ces in each scenario. These scenarios are developed relative toScenario 1 and are based on the assumption that import depen-dency is not a desirable objective. Qualitative assessment was usedfor this criterion, as can be seen in Table 14.

Standard fuel prices, at 2004 market prices, were used to estimatethe cost to the consumer under the 6 scenarios and are given inTable 11. Different units were used as appropriate for the particulartype of fuel, e.g. solid, liquid, electricity, etc. Fuel prices could beconverted into common units such as V/MWh. However, fuel usevaries for residential heating energy and electricity and, therefore,fuels may not be directly comparable as they have different purposes.

Table 12 shows costs for electricity production using varioustechnologies. Energy costs per capita were estimated for eachscenario by calculating the energy and electricity requirements perannum of the particular scenario, the fuel mix in that particularscenario and differentiated costs (at constant market prices) perunit of energy, as shown in Table 14. This indicates that Scenario 3,which represents an increase in the contribution of wood waste,has the highest cost at V502 per capita. Scenario 2, which involvesa reduction in TFC through demand management measures such asfiscal instruments, carbon rationing or greater awareness andeducation, has the lowest cost at V316 per capita.

It is assumed that energy costs represent policy options basedon constant market prices and do not include additional capitalinvestment costs, purchase costs or subsidies. This is based on theassumption that the rational consumer will opt for the scenariowith the lowest cost per capita per annum and that all costs aretransparent and are internalised in the price. Furthermore, it isassumed that scenarios based on reduction in consumption depend




Nitrousoxide (N2O)

Methane(CH4)



0.000366 0.0008273 0.00003182 0.00002386

Natural gas 0.00000093 0.0003442 0.000003722 0.00121Electricity 0.0000025 0.00000155 0.000000013 0.000000021Municipal waste

combustion (MWC)0.0005225 0.000502 0.0000134 0.00002


0.00116 0.00176 0.00005 0.00126

on behavioural change rather than energy efficiency or animprovement in energy intensity caused by investment.

The number of jobs created per average megawatt (MWa) for theoperation and maintenance of an energy technology are given inTable 13. The number of jobs created from operation and mainte-nance as a result of consumption of domestic energy and electricitywas estimated for each scenario, as shown in Table 14. Thecontribution of domestic subsidies to job creation is an importantfactor although this was not attempted in this analysis, e.g. heavydomestic subsidies in a renewable energy industry could distort thetrue value-added of the industry and, therefore, job creation.

Table 14 also shows a multi-criteria analysis matrix for domesticenergy and electricity consumption for the 6 scenarios. Fig. 1 showsthe impact matrix for domestic energy and electricity, as developedby NAIADE from the input matrix. Therefore, it can be used to illus-trate the order in which scenarios rank using the NAIADE preferenceranking. Thus, Fig. 1 indicates that Scenario 2, i.e. reduction ofconsumption by 20%, was the most preferable option and Scenario 3was the least preferable option, i.e. increased contribution of woodwaste.

Sensitivity analysis of individual scenarios or criteria was carriedout in 2 different analyses. Fig. 2 shows the results when EF is notincluded and Fig. 3 shows the results when security of supply is notincluded. This indicates that the comparative ranking does not alterwhen either criterion is removed although the individual pairwisecomparison results do.

The NAIADE results were compared with those of EF analysis tovalidate the impact of the 6 scenarios. It was found that both toolsshow that Scenario 2 is the most preferable scenario, based on theparticular criteria selected. However, NAIADE shows that Scenario3, which proposes using wood waste as a renewables option, is theleast preferable option, whereas footprinting shows that Scenario4, which proposes biomass or short rotation coppice (SRC), has thehighest EF.

In addition, footprinting shows that Scenario 5, which proposesmunicipal waste consumption (MWC) as a renewable energysource, has the second highest EF, as can be seen in Table 14 [4]. Thiscompares with MCDA, which shows that Scenario 6, which proposes

Table 11Fuel prices, at 2004 market prices (Pers. Comm., Sustainable Energy Ireland (SEI),2006).

Fuel type Unit Cost (V per unit)

Standard coal Tonne 236Wood pellets Tonne 200Kerosene oil Litre 0.49Gasoil Litre 0.48Bulk liquid petroleum gas (LPG) Litre 0.52Natural gas Kilowatt-hour (kWh) 0.03Heat pumps Kilowatt-hour (kWh) 0.14Household electricity Kilowatt-hour (kWh) 0.14

Table 12Cost per kilowatt-hour (kWh) over 15 years, at 2004 market prices [30].

Energy type Cost (V per Kilowatt-Hour)

100 MW Peat 0.068315 MW Co-firing biomass 0.04220 MW Wood waste 0.09414 MW Landfill gas 0.033150 MW Onshore wind 0.0472200 MW Offshore wind 0.06451 MW Hydroelectricity 0.05752 MW Ocean tidal 0.10485 MW Ocean energy wave 0.12741 MW Photovoltaic 0.6783


that non-organic renewable fuel options (such as heat pumps, wind,photovoltaic, tidal and wave) have the second highest impact.

In a comparative analysis of MCDA tools, as applied to bioenergyscenarios, it was found that, although NAIADE offers variousoptions for assessing scenarios, it has limited options for sensitivityanalysis [22]. Future work could involve including a range of valuesfor the selected scenarios or else adjusting the parameters of theselected scenarios.

One of the advantages of sensitivity analysis is that the model-ling procedure is based on a notion of a ‘pseudo-criterion’, whichmay result in a lack of stability and undesirable discontinuities, andsensitivity analysis can be used to balance this. However, sensitivityanalysis may be complex to manage because of the combinatorialnature of the various sets of data.

5. Discussion and conclusions

This paper shows how NAIADE, which is a multi-criteria evalua-tion technique that can be used for decision-aiding, can be applied toenergy policy scenarios and offers a template by which policyscenarios can be analysed, using a mix of qualitative and quantitativeassessment. It shows also how policy impacts can be measured usinga range of indicators, including biophysical metrics such as

Table 13Jobs created per MW ([31]; Pers. Comm., Sustainable Energy Ireland (SEI), 2006).

Jobs for Operationand Maintenance perMegawatt (O & M/MW)

Coal 0.74Oil and gas 0.7Solar thermal heat 0.97Solar photovoltaic 1.83Offshore wind 0.76Onshore wind 0.54Hydroelectricity 0.29Liquid biomass 3.1Anaerobic biomass 0.77Biomass combustion 0.29Biomass gasification 0.32Fuel production from energy crops 1.51Fuel production from forest residues 0.36Fuel production from agricultural wastes 1.3

Table 14Multi-criteria analysis summary for domestic energy and electricity scenarios.

Scenario 1 Sce

Ecological footprint (GHa per Capita) 0.146 0.11Sulphur dioxide (SO2) (Tonnes per capita) 0.000753 0.00Nitrogen oxides (NOx) (Tonnes per capita) 0.00149 0.00Other greenhouse gases – GWP (Tonnes CO2-equivalents per capita) 0.0395 0.02Security of supply Very bad badEnergy costs (V per capita) 455 316Jobs created per capita consumption 4.4 3.1

footprinting, other environmental pressures such as localised airpollutants and socio-economic impacts such as economic competi-tiveness/security of supply, costs to the consumer and job creation. Itconcludes by proposing that reduction of energy consumption usingdemand side management policies and measures should be priori-tised over supply-side measures, such as fuel substitution andrenewables.

A number of strengths or advantages of MCDA for impactassessment were identified in Section 2 from a general evaluationof the literature. A broad methodological approach to MCDA wastaken in this analysis, based on a modified procedure and theNAIADE software. However, the approach taken in this paper couldbe improved by developing weightings for different criteria ifa different MCDA technique was used and validating some of thevalue judgments taken for the qualitative assessment. This suggeststhat stakeholder input could be used and consensus reached beforesuch judgments or decisions are arrived at, although this may addto the complexity of the analysis.

It may be concluded from the approach taken in this paper thatNAIADE is useful for energy policy scenario evaluation as it allowspolicy-makers to combine discrete economic and environmentaldata with subjective qualitative judgments in an aggregated singleoutput. In addition, it could be used to either complement or insteadof reductionist techniques such as CBA or EF analysis as it allows fora more holistic approach to policy evaluation and facilitates (i) theinclusion of socio-economic as well as environmental or physicalcriteria and (ii) fuzzy evaluation through the use of linguistic qual-itative expression in addition to discrete quantitative data.

The comparison between the MCDA method and footprintanalysis suggests that NAIADE and footprinting do have somesynergies in terms of illustrating impact, as both methods indicatethat Scenario 2 has a relatively lower impact. However, it is not clearhow criteria other than the EF account for the different cumulativedecision outputs, although Scenarios 4 and 5 have high EF values asa result of land appropriation for biomass and municipal waste.

NAIADE shows that factors such as cost and job creation maydiffer between scenarios. For example, Table 14 shows thatScenario 3 has the highest energy costs and emissions of othergreenhouse gases as well as the second lowest number of jobscreated, which may explain its ranking. However, this may be partlymitigated by the fact that it has average levels of SOx and NOx anda lower EF value, compared with other scenarios.

It may be concluded that NAIADE is useful for illustrating thedifferent impacts of policy scenarios and how the inclusion ofmultiple criteria can alter the final decision output, compared witha single monetary or biophysical measure. This is done by rankingalternatives according to a set of evaluation criteria, i.e. technicalcompromise solutions. It may also be used for conflict analysis byindicating the distances of the positions of the various interestgroups, i.e. convergence of interests or coalition formations, andranking of the alternatives according to actors’ impacts or prefer-ences, i.e. social compromise solution.

The results show that MCDA, as illustrated by using NAIADE, ispreferable to a single deterministic measure as it offers a more

nario 2 Scenario 3 Scenario 4 Scenario 5 Scenario 6

2 0.122 0.157 0.127 0.1210512 0.000725 0.0008 0.00116 0.00182103 0.00169 0.00159 0.00176 0.001878 0.0438 0.0396 0.042 0.0374

More or less bad More or less bad More or less bad More or less bad502 454 446 4594.2 5.2 4.5 4.7

Fig. 1. Impact matrix for domestic energy and electricity consumption scenarios.

Fig. 2. Impact matrix for scenarios – sensitivity analysis 1.


Fig. 3. Impact matrix for scenarios – sensitivity analysis 2.


holistic perspective and is capable of facilitating both quantitativeand qualitative assessment. In addition, both tools show thatabsolute reduction and demand management should be prioritisedover renewables substitution in a policy hierarchy.

The approach may possibly be improved by stakeholderparticipation in developing scenarios and criteria or modelling theimpacts of different criteria, for example renewable energysubsidies or feed-in tariffs, global fossil fuel price fluctuations,targeted employment policies, more efficient production, etc., andthis could be attempted in future work.

Future work could include: (i) comparing NAIADE with otherSMCE or MCDA methods in order to validate or corroborate resultsand analyse the issue from different perspectives or through theuse of the most appropriate tool; (ii) allowing for more stakeholderinput; (iii) varying scenarios and criteria and (iv) undertakingfurther sensitivity analysis by offering a range for the selectedscenarios and criteria.

Acknowledgements

The authors wish to gratefully acknowledge the funding assis-tance, awarded by the Irish Research Council for Science, Engi-neering and Technology (IRCSET) under the Embark Initiative of theIrish National Development Plan (NDP) 2000–2006.

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