Generating better energy indicators: Addressing the existence of multiple scales and multiple dimensions

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Ecological Modelling 223 (2011) 41– 53

Contents lists available at SciVerse ScienceDirect

Ecological Modelling

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enerating better energy indicators: Addressing the existence of multiplecales and multiple dimensions

levgul H. Sorman ∗, Mario Giampietronstitute of Environmental Science and Technology (ICTA), Universitat Autònoma de Barcelona (UAB), Spain

r t i c l e i n f o

rticle history:vailable online 8 November 2011

eywords:conomic Energy Intensitynergetic metabolismocietal metabolismulti-scale energy accountinguSIASEM

rimary energy sourcesnergy carriers

a b s t r a c t

High energy prices and the growing concern for “Peak Oil” have put energy analysis, once again, on thefront burner. However, before speculating about possible roadmaps regarding our energy future, it wouldbe wise to develop better quantitative analyses. This paper flags the existence of systemic epistemologi-cal flaws in the current use of aggregate energy indicators and presents an alternative approach capableof dealing with the issue of multiple dimensions and multiple scales. Starting from a critical appraisalof the aggregate indicator “Economic Energy Intensity” it shows that economic and biophysical vari-ables are often correlated and that their value is determined by characteristics which can only observedacross different levels and scales. Complex metabolic systems (systems that use energy to maintain andreproduce themselves) are operating simultaneously at different scales. This implies that changes in thecharacteristics of parts, defined at the local scale, and changes in the characteristics of the whole, definedat the large scale can only be obtained after establishing a scaling mechanism in the analysis. In order todeal with the issue of scale in energy accounting, we propose to make a distinction between three differ-ent categories: (i) primary energy sources (PES) – establishing a link between energy quantities and theassociated requirement of biophysical gradients, at the large scale, on the interface black-box/context;(ii) energy carriers (EC) – defining the set of energy inputs required by technical devices for expressing

useful functions, at the local scale, within the parts operating inside the black-box; (iii) end uses (EU)the set of functions to be expressed by society across hierarchical levels for reproducing itself. Finally,the paper presents examples of quantitative results obtained using an innovative method of analysis –Multi-Scale Integrated Analysis of Societal and Ecosystem Metabolism (MuSIASEM). We conclude thatby using this new accounting method it is possible to generate a better understanding of external andinternal constraints determining the desirability and viability of the metabolic pattern of societies.

. Introduction

In this paper we first address the problems faced when tryingo develop aggregate energy indicators without properly address-ng the issue of multiple dimensions and multiple scales. To thisurpose, we use a practical example. The misleading “Economicnergy Intensity” (EEI) indicator is used as an example to expose theystemic neglect of epistemological problems often found in manyuantitative analyses related to the sustainability of complex socio-conomic systems. Economic and biophysical variables belong toifferent dimensions of analysis. They are defined within differentarratives about “what the system is” and “what the system does”

Giampietro et al., forthcoming). In relation to this point we showhat the combination of quantitative assessments referring to dif-erent dimensions of analysis into a single indicator, such as the EEI,

∗ Corresponding author.E-mail address: [email protected] (A.H. Sorman).

304-3800/$ – see front matter © 2011 Elsevier B.V. All rights reserved.oi:10.1016/j.ecolmodel.2011.10.014

© 2011 Elsevier B.V. All rights reserved.

is an unwise choice. Moreover, to make things more challenging,complex socio-economic systems are organized across differenthierarchical levels. This implies that the quantitative analysis of thecharacteristics of the system when observed at a given hierarchicallevel (e.g., the whole), are not directly reducible to the characteris-tics of the same system observed at another hierarchical level (e.g.,the parts). For this reason, before carrying out quantitative assess-ments of the characteristics of complex systems organized acrossdifferent levels and therefore requiring the adoption of multiplescales, one should carefully check: (i) the narratives (dimensionsof analysis) within which the observable qualities are defined asrelevant: what is the external referent used in the measurementscheme? Should we measure money flows or energy flows?; and(ii) the hierarchical level of analysis to which the numbers refer:what scales should be considered to get a relevant set of quantita-

tive assessments? Are we talking of flows defined over hours, yearsor centuries?

In the next section, we focus on the implications of the issue ofscale for the choice of protocols for energy accounting. Recognizing

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he importance of the issue of scale in energy analysis implic-tly entails the need of using three distinct semantic categories touantify energy forms within protocols of national accounting: (i)rimary energy sources (PES) – the narrative associated with theES category is about individuating energy transformations whichan be used to define “external constraint” in the form of a givenequirement of biophysical gradient – e.g., a ton of coal – map-ing on to a given quantity of energy – e.g., 42 GJ. Energy quantitiesxpressed in PES are used to provide aggregate assessments of theotal energy consumption of a country – e.g., measured in Tonsf Oil Equivalent. When used in this way assessment expressedn PES refer to a large scale useful to characterize the interfacelack-box/context; (ii) energy carriers (EC) – the narrative associ-ted with EC is about individuating energy transformations whichan be used to study “internal constraint” in the form of a givenequirement of energy inputs – electricity, fuels and heat – compat-ble with the identity of energy converters required for expressingseful functions. That is, a refrigerator requires a given amount ofWh of electricity, a car a given amount of MJ of gasoline, an indus-rial plant a given amount of kcal of process heat. As a consequencef this narrative the scale associated with assessments of energyuantities expressed in EC is the local scale at which these con-ersions are taking place within compartments of the society; (iii)nd-uses (EU), the associated narrative being about the integratedet of functions to be expressed by society across the various hier-rchical levels. It is impossible to associate the concept of end-usesith a single scale of observation, since the overall set of functions

n an autocatalytic process (energy used to make available energy)efers simultaneously to different scales and therefore can only beandled in semantic terms. For more on this issue see: Giampietrot al. (forthcoming).

Following the theoretical presentation we provide examplesf the results obtained when adopting an innovative protocolf accounting, the analysis of the metabolic pattern based onhe Multi-Scale Integrated Analysis of Societal and Ecosystem

etabolism (MuSIASEM). This methodology builds on the distinc-ion between energy flows which can be expressed both in relationo the semantic categories of primary energy sources and energyarriers required to guarantee a specified set of end-uses. Then werovide as an example the results of an empirical analysis (pub-

ished in Giampietro et al., in press, forthcoming) focusing on thehanges that took place in the metabolic pattern of European Union4 countries during the time period of 1990–2007.1 The character-

zation of these changes is obtained not only by keeping separatedhe dimension of analysis and across different hierarchical levels,ut also by considering the different mixes of energy carriers withinhe different sectors and the resulting energetic metabolic ratesinked to the corresponding economic labor productivity values.

In the final section we wrap-up the discussion and provide con-lusions that are relevant to policy making.

. The epistemological problems faced when developingggregate energy indicators of the performance of complexocieties

.1. The Economic Energy Intensity indicator is misleading foreasuring energetic performance

For many years now, the Economic Energy Intensity (EEI) has

een the most commonly used indicator in standard econometricnalysis to evaluate energetic performance of modern coun-ries. Yet in spite of its generalized use, we claim that it is not a

1 Luxembourg has been omitted from the analysis due to its systemic status ofutlier.

al Modelling 223 (2011) 41– 53

particularly effective indicator. To illustrate our case, we considerthe energy intensity of El Salvador, a developing country, andFinland, a developed country.

Comparing the Economic Energy Intensity of El Salvador andFinland we discover that, in the year 1997, these two countrieshad the same value of EEI equal to 12.6 MJ/$. How is it possible thattwo countries, so different in their internal characteristics, have thesame Economic Energy Intensity? In order to answer this complexquestion, we decompose the EEI into the two variables involved inits calculation (Fig. 1):

(1) The metabolic rate of added value of a country (the economicnarrative), assessed by the ratio GDP/THA, where GDP is theGross Domestic Product over one year and THA stands for theTotal Human Activity of one year (i.e., the total amount of hoursof human activity associated with a given population in 1 year).Such metabolic rate can be measured in US$/h.

(2) The metabolic rate of energy of a country (biophysical narra-tive), assessed by the ratio TET/THA, where TET is the TotalEnergy Throughput metabolized in one year and THA the TotalHuman Activity of one year. Such a metabolic rate can be mea-sured in MJ/h.

When considering these two metabolic rates separately we seethat the two ratios, GDP/THA and TET/THA, express numericalvalues that can be used as benchmarks to define typologies of coun-tries. That is, these two ratios do have “external referents” givingmeaning to the numbers within their own narrative/dimension ofanalysis. Thus, we can identify typologies of socio-economic sys-tems (Giampietro et al., in press) within:

• The economic narrative using variables referring to the eco-nomic dimension of analysis. In fact, we can define a benchmarkfor “developed countries” of GDP/THA > 1.5 US$/h (>13,000 US$p.c./year) and a benchmark for “developing countries” ofGDP/THA < 0.5 US$/h (<5000 US$ p.c./year). Thus, the pace of GDPin time is an indicator of economic activity.

• The energy narrative using variables referring to the biophysicaldimension of analysis. We can define a benchmark for “devel-oped countries” using the flow of total energy throughput intime: TET/THA > 20 MJ/h (>170 GJ p.c./year), and a benchmark for“developing countries” using the same ratio: TET/THA < 5 MJ/h(<50 GJ p.c./year). Thus, the pace of the total energy throughputin time is an indicator of socio-economic activity.

As seen in Fig. 1, both the economic and the biophysical bench-mark of Finland, a typical developed country, is one order ofmagnitude larger than the corresponding values of El Salvador, adeveloping country. These differences are invisible when adoptingthe energy intensity indicator alone, thus making it inadequate forcharacterizing the energetic performance of a given society.

We can clearly see that the problem is generated by the factthat the quantitative assessment of the EEI at 12.6 MJ/$ (Fig. 1)refers to a single hierarchical level of analysis, that of the wholenation – which we will call level n – and is obtained by combiningproxy variables that refer to two different dimensions of analysis:an economic dimension (measured in US$ of a given year of ref-erence) and an energy dimension (measured in joules). If we keepthis original information separated, rather than compressing it intoa single number, we can obtain a useful quantitative characteriza-tion. An example of this alternative approach is provided in Fig. 2.By adopting a more complex quantitative representation – a plane

based on two variables rather than a single ratio – we can preservethe original inputs of useful information.

In Fig. 2 the EEI is no longer used as a single numerical indicatorbut is represented by the location of a point on a two-dimensional

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lane with the x-axis defined as the monetary flow per hour ofuman activity (in US$/h), also referred to as the average societalconomic labor productivity (ELPSA), and the y-axis defined as thenergy flow per hour of human activity (in MJ/h), also referredo as the average societal energetic metabolic rate (EMRSA). Inhis way we can handle quantitative assessments and externaleferents from two disciplines: economic and biophysical anal-sis, respectively, without destroying valuable information. Notehat in relation to the quantitative assessment, alternatively, oneould also use more conventional variables for the two axes: mon-tary flow per capita per year and energy flow per capita per yearobtained by multiplying the respective values of the rate per hour,y the number 8760, i.e., the hours of human activity per capitaer year). However, as discussed below, in spite of their popular

se, quantitative assessments on a per capita basis are not use-ul for multi-scale analysis (see Chapter 2 of Giampietro et al., inress). When adopting an assessment of the flow per year we areorced to adopt only the national level (characteristics referring to

ig. 2. Comparing the Economic Energy Intensity (EEI) of Finland and El Salvador1998–2004) on a plane.

El Salvador and Finland for 1997.

the whole society observed at level n). On the contrary, as illus-trated below, when adopting assessments of flows per hour we canhandle descriptions referring to different hierarchical levels, suchas flow of money in the form of wages per hour, aggregate cash flowof a shop, sectoral GDP flow, or the national GDP of a country.

The weakness of the EEI indicator depends on the well knownfact that the values of the two ratios GDP/THA and TET/THA arecorrelated (see Giampietro et al., in press). Indeed, changes inthe Economic Energy Intensity of modern economies, representedon our plane, show a movement across a diagonal strip. This isillustrated in Fig. 3 for a sample of EU countries over the period1992–2004 (a full empirical analysis is given in Giampietro et al.,in press).

Being this the case, the choice of EEI as indicator for measur-

ing changes in the energetic performance of countries is extremelypoor indeed. Not only does it camouflage relevant informationregarding external constraints, such as total emissions into theatmosphere (it is an intensive indicator assessing changes per unit

Fig. 3. Trend in changes in GDP/h and energy/h for a sample of EU countries for theperiod 1992–2004.

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Fig. 4. Flow rates of energy and added value within the UK, Germany, Spain andI

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f something else and therefore does not say anything about theotal size of emissions), but also this indicator merely allows us toetect the noise over the expected correlation between GDP andET. In fact, changes in EEI only indicate the spread of points rep-esenting countries above and below the diagonal strip.

.2. Additional complication associated with the multiple levelsf organization of society

Looking at the characteristics of either Finland or El Salvadorn the plane illustrated in Fig. 2 or of the European countries onhe plane illustrated in Fig. 3, we must be aware that these dataefer to the overall average rates (GDP/THA and TET/THA) assessedt the hierarchical level of the whole society. Nevertheless, theharacteristics of the whole are determined by the characteris-ics of the lower-level compartments. For instance, the total sizef the whole society expressed in hours of human activity per yearTHA = population size × 8760), defined as level n, can be decom-osed into two lower-level parts: (i) the Paid Work (PW) sector,

.e., the hours of human activity allocated to paid work in the mar-et economy, and (ii) the Household (HH) sector, i.e., the hours ofuman activity allocated to activities outside the PW sector. As soons we define these two compartments (PW and HH) at level (n − 1),e are adding a new, distinct hierarchical level to the assessment.

hen we can observe sub-compartments expressing different char-cteristics. Indeed, if we assess the rate of GDP generation and theate of energy consumption TET, both expressed in hours of humanctivity, for these two lower-level compartments, we find that thealues for both the PW sector and HH sector are dramatically dif-erent from the corresponding average values for the society as ahole (assessed at level n). This is illustrated in Fig. 4, using the

ame plane as shown in Figs. 2 and 3 and showing the changes inhe characteristics of four EU countries (Germany, Ireland, Spain,nd UK) in time, from 1992 until 2005.

Energy data are from the IEA (2004) with an update ofnergy Balances of OECD Countries – Extended Balances Vol008 release 01 from the OECD website. The number of hoursf human activity for each subsector has been calculated fromi) number of hours usually worked per week (yearly average)

http://laborsta.ilo.org/, Category 4A Hours of work by Economicctivity), (ii) number of working weeks per year, calculated

or each country on the basis of the weeks of “paid annualeaves” and “public holidays” as published by ILO (1995), and

al Modelling 223 (2011) 41– 53

(iii) employment data (http://laborsta.ilo.org/, Category 2B TotalEmployment by Economic Activity). The GDP data has been cal-culated from the total GDP values (based on conversion frombillion US$ 2000 Constant US Dollars, IEA 20/20 Database) andpercentages of sectoral GDP (World Development Indicators,http://data.worldbank.org/indicator). A more detailed presentationof this study is given in Giampietro et al. (in press).

The characteristics represented in Fig. 4 refer to two non-equivalent views:

• A view referring to level n: In the square box labeled “Whole Soci-ety Level n” we see the same type of representation as shownin Fig. 3; the changes in the characteristics of the countries asa whole (societal average) are shown on a plane defined by theenergy metabolic rate – societal average (EMRSA in MJ/h) on thevertical axis and the rate of generation of GDP on the horizon-tal axis (in D /h). Due to the expansion of scale necessary to alsoaccommodate the representation at level (n − 1), the positions ofthe four countries result very close to each other in time, withoutbig changes.

• A view referring to level (n − 1): Within the two ellipsoids labeled“HH sector/consumption, level n − 1′′ and “PW sector/production,level n − 1′′ we see the range of values describing the changesin the characteristics of the lower-level sectors of these coun-tries. The HH sector has a much lower pace of energy metabolism(EMRHH of about 10 MJ/h) than the societal average (EMRSA ofabout 20 MJ/h) and in our method of accounting is representedas not generating any added value. On the contrary, the PW sectorhas a much higher rate of energy metabolism (EMRPW of about140 MJ/h) and of economic labor productivity (GDP generated perhour of labor in the range of 20–40 D /h) than the societal average.

The disaggregation of the characteristics of the selected EUcountries clearly shows that the use of aggregate indicators causesus to miss important information about the characteristics of itsfunctional compartments such as the household sector in chargefor final consumption and the Paid Work sector in charge for eco-nomic production. As a matter of fact, we can keep disaggregatingthe characteristics of the economy of a given country by “open-ing the black-box” of the Paid Work sector, dividing it furtherinto other functional compartments: (i) the primary and secondary(PS) productive sectors (PS), including agriculture, forestry, fishery,energy and mining (primary sector) and building and manufactur-ing (secondary sector), and (ii) the service and government (SG)sector, including all activities of the tertiary sector. As presentedin detail elsewhere (Giampietro and Mayumi, 2000a,b; Giampietroand Ramos-Martin, 2005; Giampietro et al., in press) it is possibleto carry out this disaggregation across several hierarchical levels byimplementing a mechanism of scaling in the quantitative analysis.An example of the relative characteristics of the PS sector versusthe SG sector is illustrated in Fig. 5 in relation to the changes thattook place over the various compartments of the EU14 countries inthe period 1992–2005. Investigating the metabolic profiles of thevarious subsectors that make up a socio-economic system shedslight on the energy flows that are required to express and repro-duce the structural and functional components of society in orderto remain operational (Giampietro et al., in press).

From the graph in Fig. 5 we see that the metabolic characteristics(flow rates per hour) of the various sectors are quite different. Theenergetic metabolic rates of the household and the service and gov-ernment sectors form clear clusters in the plane. The spread amongthe values of energetic metabolic rate of the primary and secondary

production sectors is more articulated as different countries maycarry out very different activities in their primary sectors. Coun-tries with a well-developed extractive industry, such as Finland,Sweden, and Belgium, generally show higher values of EMRPS

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Giampietro et al., in press). What is relevant here, however, is thats soon as we try to characterize the energetic metabolic rate of aiven society across different hierarchical levels we face a seriouspistemological challenge: the narrative about energy conversionsseful to describe energy flows at the level of the whole countrylevel n) based on primary energy sources (e.g., Tons of Oil Equiv-lent) is not equivalent to the narrative useful to describe energyows at lower hierarchical levels – e.g., level n − 2 and below (e.g.,uantities of electricity, fuels and heat). We address this challenge

n the next section.

. The problematics of energy accounting acrossierarchical levels and encompassing different narratives

.1. Three distinct semantic categories of energy forms

The definition of “energy” adopted by the pioneers of energet-cs, i.e., “the capacity to effect changes” (Rankine, 1855, p. 385), is

semantic definition that is ambiguous when it comes to a sub-tantive formalization (Giampietro et al., forthcoming). Indeed, there-analytical definition of the state space (the type of changes thatave to be represented in quantitative terms) will define the quan-itative output of the accounting. In plain words, the aggregation ofuantities of “energy forms” – e.g., potential energy such as chemi-al energy versus kinetic energy as mechanical energy – that refer toifferent narratives of either energy conversions or scales requireshe selection of a criterion of equivalence (i.e., the pre-analyticalefinition of conversion factors), which depends on the goal of thenalysis (Giampietro and Sorman, 2011).

As a matter of fact, the literature of energy analysis (energetics)eflects the historic struggle associated with the epistemologicalhallenge of handling the accounting of different energy formsAlcántara and Roca, 1995; Ayres and Warr, 2005; Ayres et al.,003; Cleveland, 1992; Cleveland et al., 1984, 2000; Cottrell, 1955;ullen and Allwood, 2010; Giampietro and Mayumi, 2004; Hallt al., 1986; Kaufmann, 1992; Odum, 1971, 1996; Slesser, 1987;lanowicz, 1986).

Abandoning the idea of using a single indicator approach fortudying the energetic performance of modern countries, weropose using an integrated set of tools for such analysis bring-

ng together variables from biophysical to economic domains. Aulti-dimensional (energy and monetary flows) and multiple-

cale approach (studying socio-economic systems across differentierarchical levels) can result more useful to study similarities and

r productivity (GDP/h) for the EU-14 over the years 1992–2005.

differences among countries (Giampietro and Mayumi, 2000a,b;Giampietro et al., in press, forthcoming). A sound accountingscheme should embrace the complex picture of energy flows inte-grated with other biophysical entities (i.e., demographic variables)and economic flows across multiple scales.

To deal with this challenge it is necessary to respect the dis-tinction between three semantic categories used to define energyquantities:

(1) Primary energy sources (PES): The narrative behind this assess-ment has to do with the analysis of external constraints. Indeed,the very concept of primary energy sources entails the existenceof an external constraint that affects the metabolic pattern ofthe system either on the supply side or the sink side. Accord-ing to the first law of thermodynamics primary energy sourcescannot be produced, they must be available in the form of favor-able physical gradients, such as stocks of potential chemicalenergy – e.g., oil reserves – or kinetic energy in the form ofwaterfalls. Therefore, the concept of primary energy sourcesestablishes a relation between a given quantity of energy andthe required availability of biophysical gradients. For example,the Tons of Oil Equivalent (TOE), a formal category of refer-ence for primary energy sources, is used to establish a relationbetween a physical quantity (1 ton of oil) and the correspondingchemical energy content (which translates into approximately42 GJ of calorific value, depending on the quality of the oil).When expressing the total primary energy source requirementof a whole society (e.g., a nation), we have to adopt a singleformal category of reference, such as Tons of Oil Equivalentor Tons of Coal Equivalent. This implies the use of conver-sion factors to aggregate into the overall assessment also otherforms of PES that are not reducible to potential chemical energy,such as hydroelectric power, wind power, and nuclear power(Giampietro and Sorman, 2011). For this reason any aggregateindicator of energy consumption expressed in terms of primaryenergy sources has two crucial characteristics: (i) it refers to thehierarchical level of the whole country, and (ii) it is not a directmeasured quantity of energy, but it is a value derived from acalculation based on a protocol of accounting.

(2) Energy carriers (EC): The narrative behind this assessment

relates to the analysis of internal constraints of the system.The quantification of energy carriers refers to a given quan-tity of energy input, which is defined in relation to the givenclass of converters that will use it. Given the set of available

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converters to generate applied power in modern society, wecan define three main categories of energy carriers that can beused to express quantities of energy: electricity (e.g., kWh), heat(e.g., kcal) and fuel (e.g., MJ). Assessments of energy quantitiesreferring to energy carriers necessarily refer to local conver-sions (energy input specific for an energy converter). Energycarriers must be produced by the energy sector from primaryenergy sources, a process that consumes energy carriers. There-fore 1 MJ of energy carrier always entails a consumption of morethan 1 MJ of primary energy source. Also in the case of energyquantities referring to energy carriers we face an epistemolog-ical problem at the moment of their aggregation. Quantities ofenergy that refer to different typologies of energy carriers, suchas 1 J of electricity and 1 J of gasoline, cannot be aggregatedwithout destroying valuable information. Airplanes cannot flyusing electricity and computers cannot run on gasoline (Smil,2003). For this reason, it is essential to keep track of the type ofcarriers required to express the various functions typical of thedifferent compartments of society.

3) End-uses (EU): This category refers to the set of functions to beexpressed by a society across the different compartments, oper-ating at different hierarchical levels and scales. Put in anotherway, the definition of end-uses characterizes the identity of themetabolic pattern of a society. It indicates the various tasksto be carried out using local flows of applied power (gener-ated by energy converters under human control). The finalgoal of end uses is to reproduce the structures and functionsassociated with the identity of a society (Giampietro et al.,forthcoming). For this reason, end-uses cannot be formalized interms of quantities of energy, which are either referring to thesemantic category of primary energy sources or energy carriers(Giampietro and Sorman, 2011).

.2. The non-equivalent quantitative representations of thenergy metabolic rate when using PES and EC assessments atower hierarchical levels

In this section we argue that the analysis of the metabolicattern at the scale of lower-level compartments and sub-ompartments is seriously hampered by the existing confusionn quantitative assessments, which can be done either in “joulesf energy carriers” or in “joules of primary energy sources”. Weefined in Figs. 4 and 5 the indicator EMRi as the MJ of “energy”etabolized per hour of labor in compartment i, with the quotationarks being put there on purpose. What semantic and formal cate-

ory should we use to assess this quantity of “energy”? In the studyf Giampietro et al. (in press), from which Figs. 3–5 are derived,ll the values of EMRi are expressed in joules of primary energyources.

However, if we express the EMRi in terms of joules of primarynergy sources metabolized in a given compartment i operatingt level (n − 2), the provided information is potentially misleadingith regard to:

The profile of power capacity: The energy converters used in thatgiven compartment do not necessarily process the amount ofenergy (expressed in joules of PES) in the form of an actual flow ofenergy carriers. For instance, an electric motor using 1 MJ of elec-tricity will be reported as having a consumption of 2.2 MJ, usingthe IEA protocol of accounting, or 2.65 MJ, using the BP proto-col of accounting, of primary energy sources. In the same way,the quantity of energy consumed in the final (household) com-

partment is overestimated when using joules of primary energysources as it includes the energy losses that occurred in theenergy sector to make the energy carriers actually used. In a way,the use of primary energy source assessments can be useful to

al Modelling 223 (2011) 41– 53

indicate the functions responsible for a given share of total con-sumption. However, these numbers are useless to discuss localefficiencies or local levels of power capacity: the efficiency of anair-conditioner refers to the consumption of kWh of electricity(energy carrier) not to the consumption of Tons of Oil Equivalent(PES).

• The profile of associated emission: The emissions attributed tothe activities carried out in a given functional compartment i donot correctly map onto assessments of specific emissions in thecompartment i. Moreover, a certain fraction of the joules includedin the assessment expressed in primary energy sources, even ifexpressed in Tons of Oil Equivalent, did not generate any emis-sion. For instance, the 3 virtual joules accounted for each jouleof electricity produced in nuclear power plants (when adoptingthe IEA approach) or the joules of virtual Tons of Oil Equivalentaccounted for each joule of electricity produced in hydroelectricplants, photovoltaic plants or wind energy farms (when adoptingthe partial substitution method used by BP) are not generat-ing the CO2 emission associated with the real burning of tonsof oil.

To make things more difficult, we also have to deal with datainconsistency in international statistics. The various internationaldatabases used by analysts suffer from lack of consensus and coher-ence, causing notable differences in data resulting from differentaccounting schemes (e.g., BP statistics versus OECD/IEA, 2004)(Giampietro and Sorman, 2011).

For this reason, we propose a dual accounting based on (i) pri-mary energy sources and (ii) energy carriers. The dual system ofaccounting is necessary in order to be able to perform two types ofsustainability checks over the feasibility of energy scenarios:

(1) An accounting based on primary energy sources assesses theinteraction that the society as a whole has with its context. Thisaccounting is required to check the compatibility with exter-nal biophysical constraints, both on the supply (availability ofresources) and sink side (the environmental impact, such asCO2 emission).

(2) An accounting based on energy carriers allows an assessment ofthe transformations inside the various compartments of soci-ety. It is required to check the internal constraints, i.e., thetechnical feasibility of the set of conversions that have to takeplace in the various compartments to guarantee the requiredset of end-uses.

As a basis for our dual accounting, we have selected the par-tial substitution equivalent method (the protocol adopted by BP).According to this protocol, the primary energy source equivalentof hydro electricity, nuclear electricity, and any other electric-ity generated from non-fossil energy sources is accounted forby considering the equivalent amount of fossil fuel reference(oil) that would be needed to generate that same amount ofelectricity.

This choice is motivated by the perceived need to respect theprinciple that 1 J of electricity (“vis viva”) cannot be equivalent to1 J of primary energy source (potential thermal energy in chemi-cal substance) and to give hydro (and nuclear) sources a fair andcomparable scale in relation to the other primary energy sources,notably the predominant use of fossil energy.

3.3. Illustration of the proposed scheme of PES–EC–EU

The proposed scheme of energy accounting is illustrated inFig. 6. The characterization of the chain of energy flows is basedon the distinction between primary energy sources (quantity andmix), energy carriers (quantity and mix) and the identity of the

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Fig. 6. The PES–EC–EU sche

unctions to be expressed associated with the pre-analytical defi-ition of end-uses (Giampietro et al., forthcoming). The quantitiesnd mixes of energy throughputs in Fig. 6 refer to Spain (nationalevel) in the year 2003.

According to the scheme presented in Fig. 6, the boundaries ofhe system are defined in relation to the set of primary energyources entering into the energy scheme. They can broadly be cat-gorized by fossil fuels, renewable sources and nuclear sourcesbottom of Fig. 6). All primary energy sources are expressed iniophysical quantities. We then assign an energy value to the bio-hysical quantities of PES (a given biophysical gradient makes itossible to generate a gross flow of energy carriers). After indi-iduating a PES of reference, in this case we have chosen Tons ofil Equivalent we convert the various primary energy sources into

single reference value, thereby assigning an energy equivalento “non-thermal” primary energy sources, i.e., wind, hydro, biofu-ls, and nuclear on the basis of the partial substitution equivalentethod (the BP protocol). For this calculation the average efficiency

onversion factor in OECD countries of 38.5% is used. This valueranslates into a conversion factor: 1 MWh = 0.086 toe/0.385.

Energy carriers in Spain can be broadly categorized into threeain types: electricity, heat and fuels. The three main categories

ustrated for Spain for 2003.

of possible forms of carriers are used for guaranteeing the requiredend-uses to the society, as illustrated in the upper part ofFig. 6.

We thus see that in the year 2003 Spain consumed 239 TWhof electricity, 1400 PJ of heat and 1776 PJ of fuel; flows of energycarriers that were required for the operation of its power capacityinside the different socio-economic compartments. To supply thisflow of energy carriers Spain used 6234 PJ (TOE) of primary energysources.

Regarding end uses, the system has been represented by thevarious sector of the society expressing specific functions. Withinthis grammar, five basic sectors (functions) are used: (i) stabilizingthe supply of energy carriers and material inputs – Energy Sec-tor and Mining (EM); (ii) stabilizing the supply of food and fibers– agricultural sector (AG); (iii) stabilizing the supply of technol-ogy and infrastructures – building and manufacturing sector (BM);(iv) guaranteeing the transaction activities required for the repro-duction of societal institutions – service and government sector

(SG); (v) guaranteeing the reproduction of humans and the supplyof working hours to the Paid Work sectors – Household sector (HH)– for more on this approach see Giampietro et al. (in press). Thesefunctional compartments operate at different EMR rates (expressed

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48 A.H. Sorman, M. Giampietro / Ecological Modelling 223 (2011) 41– 53

Fig. 7. Energetic metabolic rate (EMR) and economic labor productivity (ELP) of the productive sectors of the EU14 in 1990 and 2007 based on primary energy source (PES)a

he

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nd energy carrier (EC) accounting.

ere in terms of EC) which can be associated with a characteristicnergetic metabolic profile.

In this representation the external constraints (the PES–EC setf conversions) refer to the interface of the economic system (thelack-box) with its environmental context or boundary limits.nly when adopting this representation can it be concluded that

here indeed exist biophysical limits to the expansion of economicrowth due to the potential instability of this interface. As a conse-uence, the growth of world economy is bounded by natural limitsn the pace of extraction of material and energy sources and byatural limits on sink capacities for the assimilation of the wasteenerated. When limited by either supply side and/or sink side con-traints, the different compartments of a socio-economic systemsperating within the black-box have to change their characteristicsr their relative size in order to preserve the functionality of thehole (the EC–EU set of conversions). Basically, the black-box (the

unctional whole represented by society) and the organizationalattern within it (representing the various economic sub-sectors),ave to be fine-tuned according to these external constraints. Whenhis re-adjustment takes place, the characteristics, the relative sizend the network of interactions of the subsectors of a society haveo be adjusted in an integrated way, across levels and scales, inrder to match the limits imposed by external constraints.

In the next section we provide a practical example, taken fromur analysis of changes in the metabolic pattern of EU14 countries,

ndicating the importance of carrying out the distinction betweenssessments of quantities of energy referring to the semanticategory PES and quantities of energy referring to the semanticategories EC.

4. Implementing a more complex method of analysis:keeping the distinction between PES–EC–EU across levels

4.1. Accounting for the energy metabolism of the EU-14 countries

Using the previously presented scheme and keeping the dis-tinction between quantitative assessments of energy throughputsin terms of PES and EC, we now describe the metabolic pat-tern (compartment-specific EMRi in MJ/h and ELPi in D /h) of theEU14 for 1990 and 2007 for two compartments, the primary andsecondary production sectors (PS) (Fig. 7) and the service and gov-ernment sector (Fig. 8).

Data presented in this section have been used to charac-terize the transformation of primary energy sources used inthe mix (e.g., coal, oil, gas, renewables and waste, wind photo-voltaic) into the supply of a given mix of energy carriers – heat,electricity or fuel (a detailed explanation of the protocol is avail-able in Giampietro et al., forthcoming). To determine incomingPES sources we have used the Energy Database from Eurostat(http://epp.eurostat.ec.europa.eu/portal/page/portal/energy/data/database), in particular the annual data on Supply, transfor-mation, consumption for solid fuels (nrg 101a), oil (nrg 102a),gas (nrg 103a), renewables and wastes (total, solar heat,biomass, geothermal, wastes) (nrg 1071a); and renewables(wind, photovoltaic) (nrg 1072a).

These sources have been linked to the specific mix of EC thatthey produce. In the same way the assessment carried out in ECis referring to a well specified mix of EC (electricity, heat and fuel)across the different levels of analysis. Ultimately, the various mixes

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ig. 8. Energetic metabolic rate (EMR) and economic labor productivity (ELP) of theource (PES) and energy carrier (EC) accounting.

f EC have been subcategorized according to the categories of “endses” (Final Energy Consumption) such as: Agriculture, Industryto include Building and Manufacturing and, Energy and Mining),ervices and Government, and Household.

As regards to the productive (PS) sector, illustrated in Fig. 7, theertical axis represents the energy metabolic rate (in MJ/h) calcu-ated using a dual accounting in both: (i) joules of primary energyources, thus including also the energy losses for generating elec-ricity, fuel and heat (top graph), and (ii) joules of energy carrier,onsidering only the energy carriers used in the sector to generateseful work (bottom graph). On the horizontal axis of each graph,he economic labor productivity is expressed in terms of D /h ofctivity. The size of each disk indicates the total number of hoursllocated to this specific sector PS.

As expected, the energy throughput per hour is lower when theMRi is measured in joules of energy carrier rather than in joulesf primary energy source. In fact, when assessing the throughputn joules of energy carrier – directly used at the local scale – were not charging the productive sector for all of the energy lossesssociated with the generation of energy carriers used by otherectors. But note that in this way we are not counting the entireosses for making energy, i.e., the energy carriers used by the powerlants to produce energy carriers not used in PS.

This potential confusion in the energy assessments of the pro-uction sector shows once again the risk of providing (and using)nly a single quantitative assessment of the type “one size fits all”n energy statistics. It is possible to do an accounting attributing

o the PS sector only the losses referring to its own operation (thessessment in EC given in Fig. 7 refers to the activities carried outn the sector in terms of power capacity). However, this can beone ONLY IF at the same time we have another non-equivalent

ce and government sectors of the EU14 in 1990 and 2007 based on primary energy

accounting dealing with the assessment of where the actual lossesare taking place (e.g., when looking for a spatial location of thelosses). When comparing countries operating with different mixesof primary energy sources, different mixes of energy carriers, anddifferent levels of import and export of energy forms, it is simplyimpossible to provide a useful characterization of the differencesin their metabolic patterns, by referring to just one bulk value ofenergy use, which is based on a single set of energy accounting –as presently attempted by OECD/IEA (2004) using the category of“joules of energy commodities”.

Another interesting observation, when looking at the trend ofchanges over the years, is that the productive sectors of the EU14have clearly experienced an intensification of their energy through-put (EMRPS), whether we calculate it in terms of joules of primaryenergy source or joules of energy carrier. The initial agglomera-tion of the cluster of countries in the year 1990 has spread acrossthe diagonal by 2007 because of increasing differences in ELPPS.This shows that it is simply untrue that the PS sector is reduc-ing its energy consumption because of “better and more efficienttechnology”.

The same dual accounting of the metabolic pattern of EU14 isillustrated in Fig. 8 for the service and government sector. Notethat the values of EMRSG are much lower than the values of EMRPS,to a point that the scale on the vertical axis for the service andgovernment sector in Fig. 8 is approximately half of that used forthe productive sectors in Fig. 7.

Also when considering the dual reading of EMRSG we see a clear

difference in the picture given by the two accounting methods.Notably, the differences among countries in the EMRSG are muchless pronounced when expressed in joules of energy carrier ratherthan in joules of primary energy sources. An especially visible result

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F ted byf

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ig. 9. (a) Energy carrier mix profiles per sector per country for 1990 (image generaor 2007 (image generated by Many Eyes Programme IBM).

s the convergence to a common value of EMRSG (expressed in joulesf energy carrier) in the year 2007. In fact, in the PS sector differentixes of activities (e.g., forestry and pulp industry, heavy metal-

urgy, industrial production, versus textile and computer industry)pecific for countries operating in different biophysical contexts dontail differences in the intensity of the use of energy carriers. Onhe contrary, in the SG sector the tasks to be performed are more oress similar among countries. Moreover, the technology used in thisector is increasingly becoming uniform, as indicated by the facthat the EMRSG of the various EU countries are converging around

value of 60 MJ/h, when measured in joules of energy carrier.

Nevertheless, as shown in Fig. 8, when assessing the value of

MRSG in joules of primary energy sources we do find a ratheriversified set of values for the EU14 countries. This is becausen assessment of EMRSG expressed in joules of PES includes the

Many Eyes Programme IBM). (b) Energy carrier mix profiles per sector per country

effect of the differences in the mix of primary energy sourcesused to generate energy carriers – e.g., it includes the primaryenergy losses for electricity generation. This is extremely clearwhen looking at the changes in time of Belgium, France and theNetherlands.

4.2. The profile of energy carrier use in the various compartmentsof society across levels

Implementing the method of dual accounting allows us tovisualize the mix and the diversity of primary energy sources in

each country (in terms of PES), the subsequent flows of energycarriers (EC) and the mix of EC required by the various compart-ments in relation to end uses, determined by the set of functionsthat society has to express. When performing this analysis we see

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hat economic and social development do not only entail differentevels of consumption of energy carriers (in aggregate terms) butlso a different relative profile of energy carrier use.

The differences in the pattern of use of energy carriers (broadlyategorized into electricity, heat and fuel), in 1990 and 2007, forach sector of the 14 considered countries is illustrated in Fig. 9and b. In these two figures, the overall size of the pies illustrates theotal amount of energy carriers consumed in each compartment.he relative sizes of the slices within each pie indicate the relativerofile of consumption of energy carriers within the mix.

.2.1. The profile of energy carrier use in the agricultural sectorOn average the typical European cluster agglomerates around

n energy carrier mix at: 60% fuel, 20% electricity and 20% heatources for the agricultural sector. Fuel is mainly used for tractorsnd machinery in carrying out intense agricultural practices, fol-owed by heating sources used for greenhouses. There are somexceptional cases related to particular specializations in agricul-ural production, such as heat in the case of the Netherlands in007 at a value of 65%; or electricity, for the United Kingdom in007 at 40%. The results of this analysis reveal that the Netherlands

s also an exceptional case in terms of overall energy throughput inhe agricultural sector (the overall sum of all the energy carriers)n comparison to the rest of the EU countries, followed by France,pain, Germany and Italy. In generally, over the years, the AG sectoras faced intensification in terms of energetic throughput. How-ver, in comparison with the other economic sectors it still has aery low throughput of energy.

.2.2. The profile of energy carrier use in the productive sector

The productive sectors are mainly dependent on heating sources

hat drive the industry. They are primarily used for intense manu-acturing, steel production and other heavy industries. As a result,he PS sector is the socio-economic sector with the highest overall

Fig. 10. The metabolic patterns of the EU14 countries for the agric

al Modelling 223 (2011) 41– 53 51

energy throughput. The requirement of electricity has increasinglybeen gaining importance in overall fraction over the period of theanalysis. Yet, as observed for the 17 year span of analysis, mostcountries (e.g., France, Belgium, Germany, Netherlands and UK)have decreased their overall total throughput of energy carriers inthe productive sectors. Only few countries, notably Spain and Italy,have increased their throughput in productive sectors, probablydue to the booming of building and construction activities after the1990s.

4.2.3. The profile of energy carrier use in the service sectorPercentagewise, the majority of the service sector is dominated

by heating sources (used for heating buildings), fuel sources (in ourmethod of accounting the transport sector – road and freight – isincluded in the service sector), followed by electricity. In almost allof the countries the consumption of energy in the service sector –in absolute terms – has expanded immensely, indicating the cleartransition from an economy based on the production of goods, to amore knowledge based society based on services.

4.2.4. The profile of energy carrier use in the household sectorThe general percentage distribution of energy carriers illustrates

a cluster at around an average value of: heat of 40–50%; electricityof 15–20% and fuel of 30–40% for the household sector. The fuel isclearly utilized for transportation (due to large private car owner-ship). The throughput of fuel in the household sector (in proportionto the rest of the carriers) is expectedly higher in countries withhigh private car ownership (e.g., Italy).

4.3. Integrating energy and economic analysis

An overview of the performance of the various socio-economicsectors is given in Fig. 10. The sizes of the bubbles illustrate thetotal amount of hours dedicated to each activity for each country.

ultural, productive, service and household sectors for 2007.

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he plane used for the representation is similar to that illustratedn Figs. 4–7: on the x-axis we have the economic labor produc-ivity (D /h) and on the y-axis we have the energy metabolic ratethe energy throughput consumed per hour in the sector in MJ/h).ote, however, that the energy metabolic rate is here measured

n joules of EC and not in joules of PES (in relation to the analy-is presented in Fig. 9a and b). The performance of the differentectors resulted markedly different to the point that we had todopt different scales for the vertical axes (energetic metabolicates in MJ/h).

.3.1. Metabolic pattern of the agricultural sectorThe energetic metabolic rate of the agricultural sector is high-

st in regions of traditionally intense practices, primarily led by theetherlands, followed by Denmark and Sweden (the high techni-al capitalization is due to cattle ranching for dairy purposes). Inhese countries, the economic output also results high thanks tohe option of export of high quality cattle/dairy products.

.3.2. Metabolic pattern of the productive sectorThe productive sector is the most intense sector in terms of

nergy throughput per hour. Depending on the type of manufac-uring there is much variability within the sector itself. In fact,he activities can range from heavy industry and large manufac-uring such as the steel, metallurgy, automotive sectors as seenor example in Austria and Belgium to “sophisticated manufac-uring” including high tech industries namely electronics thatequire skilled labor such as in the Netherlands and Denmark. Evi-ently, countries having energy-intensive industries (e.g., forestry,aper and pulp, metallurgy) have a higher EMR in this sector.owever, to carry out this “fine tuning” analysis of differencesmong EU14, we should have used data referring to lower levelub-sectors (level n − 4 or lower) in order to be able to individ-ate differences in technical performance of individual industrialectors.

.3.3. Metabolic pattern of the service sectorThe service sector has a relatively low energetic metabolic rate

clustering at around 60 MJ/h – with relatively high economicutput rates – at an average of 60 D /h for the analyzed EU14 coun-ries. Thus, in relation to the consumption of energy carriers, theest economic performance is achieved by the service sector. This

ndeed explains why developed (European) countries have beenhifting their heavy industries outwards (to developing countries),witching to a service-based economy. In this way, they can gener-te more added value while using a lower energetic throughput.ut in another way, rising labor costs and higher energy costsave pushed manufacturers to move to Eastern Europe or Southast Asia and China. Such a drive toward cheaper production pat-erns has resulted in a massive externalization of the most energyntensive production processes (the PS sector). This allowed anmportant diversification in the service sector activities associ-ted with important increases in the fraction of GDP, paid for byn increasing dependency on import for the supply of requiredoods.

.3.4. Metabolic pattern of the household sectorThe household sector, which is not represented as contribut-

ng to the formal economy (within this system of accounting ofonetary flows, based on sectoral GDP, the HH sector does not

ontribute directly to the generation of GDP), has the lowest energyhroughput per hour in comparison with the other sectors. This is

ue to the very large number of hours of human activities spent

n this sector including activities such as eating, sleeping, personalare and household chores. Due to the high dependency ratio inodern societies (more than 50% of the population is not in the

al Modelling 223 (2011) 41– 53

Paid Work sector) and the light work loads (about 1800 h/year,which is 10% of the total hours in a year) more than 90% ofhuman activity of a modern society is taking place in the householdsector.

5. Conclusions

The quantitative analysis of complex socio-economic systemsentails facing several systemic epistemological problems, a factmaking challenging the use of aggregate indicators, obtained bymixing variables referring to different dimensions of analysis andbelonging to different narratives about “what the system is” and“what the system does”. The popular indicator Economic EnergyIntensity is a clear example of this risk. An additional challengeis represented by the fact that complex socio-economic systemsare organized on different hierarchical levels. This implies thatthe quantitative analysis of the characteristics of the system whenobserved at a given hierarchical level (e.g., the whole – nationallevel analysis) is not directly reducible to the characteristics ofthe system observed at another hierarchical level (e.g., the parts –the subsectors of the socio-economic system). When dealing withenergy analysis the issue of multi-scales entails that quantitativeinformation useful to check internal constraints – i.e., how manyenergy carriers (EC) do we need and in which mix – referring to thelocal scale – is not equivalent and easily reducible to the quanti-tative information useful to check external constraints – i.e., howmany primary energy sources (PES) do we need and in which mix– referring to the scale of the whole society.

To avoid this impasse, an innovative approach based onthe Multi-Scale Integrated Analysis of Societal and EcosystemMetabolism (MuSIASEM) approach can be used in order to generatean integrated analysis of the metabolic pattern across hierarchi-cal levels. The results of an empirical analysis of the changes thattook place in the metabolic pattern of European Union 14 countrieshave been used to illustrate the point of this paper. By applyingthe proposed protocol, it is possible to study changes: (i) keepingseparated the dimension of analysis, but still linking, within eachsector, energetic metabolic rates to the corresponding economiclabor productivity; (ii) the non-equivalent characterizations refer-ring to different hierarchical levels; and (iii) considering the mix ofenergy carriers within the different sectors.

The examples presented in the text, in our view, show that itis indeed possible to develop new accounting methods capable ofgenerating better energy indicators by addressing the existenceof multiple scales and multiple dimensions. Further developmentof this new class of indicators will make it possible to achievea better understanding of both external and internal constraints,determining the desirability and viability of the metabolic patternof societies.

Acknowledgements

The authors gratefully acknowledge the financial support pro-vided by (i) the EU for the Project SMILE (Synergies in Multi-scaleInter-Linkages of Eco-social systems) FP7-SSH-2007-1 (Project no.217213), (ii) the Agència de Gestió d’Ajuts Universitaris i deRecerca of the Generalitat de Catalunya (AGAUR), SGR2009-594;(iii) the Ministry of Oil of Norway for sponsoring the special ses-sion on Energy Statistics at the VII Biennial International WorkshopAdvances in Energy Studies (October 2010) in which this paper waspresented. We also would like to thank Jun Elin Wik Toutain of the

Oslo Group for the useful insights provided; Luisa Picozzi, CesareCostantino, Giusy Vetrella, and Aldo Femia of the Italian NationalInstitute of Statistics (ISTAT) for the useful discussions on the topicof energy statistics and for hosting Alevgul Sorman, and to the

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nonymous reviewers who provided useful comments to the firstraft of this paper.

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