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Journal of Cleaner Production 48 (2013) 39e47

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Journal of Cleaner Production

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Investigating the Carbon Footprint of a University - The case ofNTNU

Hogne N. Larsen a,*, Johan Pettersen a, Christian Solli a, Edgar G. Hertwich b

aMiSA AS, Beddingen 14, NO-7014 Trondheim, Norwayb Industrial Ecology Programme, Norwegian University of Science and Technology (NTNU), NO-7491 Trondheim, Norway

a r t i c l e i n f o

Article history:Received 7 March 2011Received in revised form5 October 2011Accepted 6 October 2011Available online 17 October 2011

Keywords:Carbon FootprintEnvironmental extended InputeOutputanalysisPublic servicesUniversities

* Corresponding author. Tel.: þ47 91 73 09 52.E-mail addresses: hogne.n.larsen@ntnu.no, hogne@URL: http://www.misa.no

0959-6526/$ e see front matter � 2011 Elsevier Ltd.doi:10.1016/j.jclepro.2011.10.007

a b s t r a c t

In this paper we apply an Environmental Extended InputeOutput (EEIO) model to calculate the CarbonFootprint (CF) of the Norwegian University of Technology and Science (NTNU). Results show that the CFof NTNU is very significant with an average contribution of 4.6 tonnes per student. In particular, thepurchase of large amounts of equipment and consumables for scientific use is found to be an importantcontributor to this. In the paper we also derive per-department CFs, enabled by the standardizedstructure of the financial accounting system used by the university. Results show large variations in theCF of the different faculties. Social Science and Humanities have a significantly lower CF per studentcompared to Natural Science, Engineering, and, in particular, the Faculty of Medicine. The single mostimportant CF contributing input to the university is, however, allocated to the property departmentregarding the use of energy and other building related activities. Also, the CF structures of the depart-ments/faculties show large differences that indicate that different mitigation strategies are needed.

� 2011 Elsevier Ltd. All rights reserved.

1. Introduction

There has been an increasing focus on the evaluation of theenvironmental performance of businesses, organizations andgovernmental institutions as a means to focus environmentalmanagement efforts and track development over time (Huang et al.,2009;Wiedmann et al., 2009; Berners-Lee et al., 2010; Lenzen et al.,2010). One type of institutionwhere there has been a specific focuson sustainable achievements is universities. This focus is high-lighted by a specific conference (Environmental Management forSustainable Universities, EMSU (Ferrer-Balas et al., 2010)) andseveral rankings (e.g., the Engineering Education for SustainableDevelopment, EESD) on the environmental performance ofuniversities. Most of these initiatives have quite broad scope: therole of universities in creating knowledge, integrating sustainabilityin educational and research programs, and the promotion ofenvironmental issues to the society (Lozano, 2010; Stephens andGraham, 2010; Waas et al., 2010). However, a few more specificscientific analysis regarding carbon (Baboulet and Lenzen, 2010;Klein-Banai et al., 2010; Thurston and Eckelman, 2011) andecological (Venetoulis, 2001; Wood and Lenzen, 2003; Conwayet al., 2008; Klein-Banai and Theis, 2011) footprinting of universi-ties are available. Furthermore, a wide range of CF inventories for

misa.no (H.N. Larsen).

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universities, often applying bottom up collection of data incombination with fixed CF intensities from online carbon calcula-tors, have emerged.1 Note that most of these studies are not directlycomparable to the one applying EEIO modeling, as they onlyinclude selected indirect, scope 3, contributions.

The Norwegian University of Technology and Science (NTNU) isthe second largest university in Norway. It consists of two maincampuses, covering most of the activities, and is located in the cityof Trondheim. More than 20 000 students and 5500 employees aredivided into seven faculties and 53 departments. As the nameindicates, we find a strong focus on science and technologicaleducation at this institution. In 2005, the NTNU administrationintroduced an environmental program based on the ISO 14001guidelines and identified four target areas: energy, transport, wasteand procurement. For the three first target areas, data now existthat can be used for deriving indicators on the environmentalperformance. For emissions relating to procurement, however, nocalculations have been made. There are also other potential gaps inthe environmental programs, perhaps the most important of whichis the emissions related to buildings (construction, maintenanceand other inputs in operating a building, besides energy).

One indicator introduced in GHG accounting is the CarbonFootprint (CF) (Wiedmann and Minx, 2007; Weidema et al., 2008;

1 Due to initiatives like the Americal Colleage & University Presidents’ ClimateCommitment.

Table 1Key elements of EEIO model used.

Key elements Description Comments

Year of EEIO data 2005Number of EEIO

sectors120 (4 þ 58þ58) Process þ domestic

þ importsHybridized processes Fuel for transport, heating oil,

electricity, district heatingGHG gases included CO2, CH4, N2O, CO, HFC, PFC, SF6 For imports only CO2,

CH4, N2OTreatment of imports Assumed to be represented by

the German economyOther adjustments Price adjustments, Trade and

Transport margins, CapitalFor Capital: onlyconsider depreciation

2 NACE is derived from the French title “Nomenclature générale des Activitéséconomiques dans les Communautés Européennes” (Statistical classification ofeconomic activities in the European Communities).

H.N. Larsen et al. / Journal of Cleaner Production 48 (2013) 39e4740

Peters, 2010). As a measure covering all direct and indirect GHGemissions, more specifically scope 1 (direct process emissions),scope 2 (indirect emissions from the purchase of energy and scope3 (other indirect emissions caused by the purchase of goods andservices) according to the GHG protocol (WRI and WBCSD, 2004),the CF indicator has proven effective as a suitablemeasure in awiderange of studies, ranging from global (Peters and Hertwich, 2008;Hertwich and Peters, 2009; Davis and Caldeira, 2010), regional(Peters and Solli, 2010), national (Peters and Hertwich, 2006;Wiedmann et al., 2010) to the sub-national level (Wier et al.,2001; Druckman and Jackson, 2009; Larsen and Hertwich, 2010b;Lenzen and Peters, 2010). Environmental Extended InputeOutput(EEIO) modeling (see e.g., Minx et al. (2009)), has proven to be themost promising methodology in calculating CFs on the scalesindicated (Peters, 2010).

In this paper we investigate the CF of NTNU using EEIOmodeling, covering all aspects of the university’s activities. Weinvestigate the total CF, benchmark results, and identify structuralcontributions to the total. We further investigate the CF by refiningthe results to the different contributing departments/faculties. Thepaper starts with a short introduction to the methodology, we thenpresent the results of the analysis, and finally we discuss the mainfindings and potential for future work.

2. EEIO modeling

InputeOutput Analysis (IOA) was introduced in the 1930s(Leontief, 1936). A few decades later work begun on adding envi-ronmental information, both internalized (Leontief, 1970) and asexternalities (Ayres and Knese, 1969). Further developmentsincluded work by Dantzig (1976) and Miller and Blair (1985). Theinclusion of environmental information gave birth to Environ-mental Extended InputeOutput (EEIO) based modeling. Recentdevelopments are numerous; on multi-regionality (Peters andHertwich, 2008; Hertwich and Peters, 2009; Stromman et al.,2009; Tukker et al., 2009; Wiedmann, 2009; Wiedmann et al.,2010), hybridization (Treolar, 1997; Nakamura and Kondo, 2002;Suh et al., 2004; Suh and Huppes, 2005; Stromman and Solli,2008; Lenzen and Crawford, 2009) and on sub-national levels(Lenzen et al., 2007; Larsen and Hertwich, 2009; Wiedmann et al.,2009; Lenzen and Peters, 2010). A thorough overview of thedifferent IOA applications to environmental analysis is provided byMinx et al. (2009).

For the purpose of calculating the CF of a complex enterprisesuch as a university, EEIO modeling (Munksgaard et al., 2005;Peters and Hertwich, 2008; Hertwich and Peters, 2009; Minxet al., 2009) has proven very useful (Wiedmann et al., 2009;Lenzen et al., 2010). There are several reasons why an EEIO basedmodel was considered an appropriate calculation methodology inthis work; firstly, the focus on public services mandates a need totake into account non-physical flows. For this purpose standardLCAs are found insufficient (Suh and Huppes, 2002; Junnila, 2006).Secondly, the financial framework applied by governmental entitiesprovides both a detailed and a standardized format suitable forEEIO modeling. Thirdly, EEIO modeling works effectively inproviding good quality, timely estimates of reliable accuracycompared to the more detailed, time-consuming, LCAs.

While EEIO modeling works very well to provide a completeoverview on the contributions of the total CF, it performs less wellon detail (in Norway limited to 58 sectors), improvement options(all products within a category are assumed to have identical CF)and deriving time series (although time-efficient to derive, alsovulnerable to price variations). Further, most EEIO models are a fewyears old due to the time-consuming construction of increasinglycomplex models so that changes in production technology from

year to year are not sufficiently captured. Several authors nowapplycombinations of EEIOAs and LCAs, termed hybrid-LCA (Heijungsand Suh, 2002; Suh and Huppes, 2005), to compensate for theweaknesses of both (Marheineke et al., 1998; Treloar et al., 2000;Stromman et al., 2006; Michelsen et al., 2008; Rowley et al.,2009; Bilec et al., 2010; Mattila et al., 2010).

For the EEIO model used for the NTNU study, we hybridized themodel for all Scope 1 and Scope 2 GHG emissions (WRI andWBCSD,2004). The model is similar to that used for municipalities andcounties, as described in Larsen and Hertwich (2009), and hasfurther been refined (Solli et al., 2009) for the purpose of assessingthe CF of central government entities such as universities. Capital isinternalized and import fractions included (represented by theGerman technology). In total, 120 EEIO sectors are available in themodel; two scope 1 contributions (combustion of fuel and heatingoil), two scope 2 contributions (the purchase electricity and districtheating), 58 domestic EEIO sectors and the corresponding 58 EEIOimport sectors covering all other purchases of goods and services(Scope 3) using the standardized NACE industry classification.2

Some key elements of the model are summarized in Table 1:The financial account for NTNU from 2009 was used in this

analysis. It constitutes of more than 200 categories covering bothpurchases of goods, services, and investments. All elements thatcontribute to the CF are listed in Appendix B. For investments, thedepreciation values in the accounts were used, obtained by dividingthe CF per year equally over the economic life time expectancy ofthe different products. A key part of applying themodel is matchingthe data from the financial account to the EEIO sectors in AppendixA. In most cases the matching causes no problems (for instancematching the purchase of publications to the “Y22 publishing,printing and reproduction of recorded media” EEIO sector, while inother cases more information on the composition of purchasingcategories is needed. There is also the possibility of matching onepurchasing category to several EEIO sectors. For instance, thedemand for most types of equipment is quite aggregated in thefinancial account and therefore distributed over several EEIOsectors.

When the matching is complete we constructed the totaldemand NTNU has on the economy. The data was then priceadjusted to fit the 2005 EEIO data using the consumer price index.The numbers are further converted to basic price to fit the model,and the trade and transport margins (TTM) are distributed to therelevant sectors. Using EEIO modeling to calculate the inter-industry flows (supply chains) in combination with GHG emissionintensities for each EEIO sector, we are now able to generate thecomplete CF of the university. Using the financial account as the

Table 2Key figures of the NTNU analysis.

Indicator

Number of students w20000Number of employees w5500Budget 4.7 billion NOKa

Operational expenditures 1.34 billion NOKa

Investments 0.42 billion NOKa

Carbon Footprint (CF) 92 kilotonnes of CO2eCF per student 4.6 tonnes of CO2eCF per employee 16.7 tonnes of CO2eCF per purchase (Op.þInv.) w0.05 CO2ekv. per NOKa

a 7.8 NOK ¼ 1 Euro.

H.N. Larsen et al. / Journal of Cleaner Production 48 (2013) 39e47 41

main source of data has clear advantages; it allows an easy yearlyupdate of the CF once the financial data are available, and avoidstime-consuming bottom up collection of data (with the exceptionof energy data that is hybridized for better detail). Furthermore, itcovers all types of purchases and activities avoiding tradeoffs andrebound effects, and secures an assessment of this in a methodo-logical consistent manner.

3. Results

3.1. Overall results

The total CF of NTNU was calculated to be 92 kilotons of CO2equivalents for the year 2009. This includes the emissions from alloperational expenditures and activities in addition to the CFresulting from investments. Some key results regarding NTNU, alsorelating them to the CF found, is illustrated in Table 2.

Normalization of the results per student and per employeereveal some interesting findings. From Larsen and Hertwich(2010a), we find the total CF of an average Norwegian citizen tobe approximately 15 tons of CO2 equivalents, using a earlier versionof the model applied here. The NTNU numbers hence indicate thatfor students, the CF resulting from the higher education provided tothem, 4.6 tons per student, is a very significant part of their total CF.For employees, a CF of 16.7 tons indicates that the averageemployee is responsible for approximate as much emissionsthrough their job at NTNU as they are through their private life,although this of course varies widely with different positions.Note that these employee related perspectives are not strictlycomparable, as the NTNU CF is an upstream contributor to the totalCF of the average Norwegian, hence double counted. Furthermore,a CF of 0.05 kg CO2 equivalents per NOK (Norwegian currency; 7.8NOK ¼ 1 Euro) indicates that the university has a cleaner mix of

Energy: Electricity, district heating, heating oil

Travels: Employees, students, car allowance, etc.

Buildings: Contruction and maintanance

Equipment: Sci.&Tech., computers, machinery, etc.

Consumables: Office sup., teaching sup., food, books, etc.

Services

Other

Fig. 1. Structural compositi

purchases/activities than most other sectors, especially comparedto primary and manufacturing industries often in the range of0.05e0.1 kg CO2 equivalents per NOK. However, it is still signifi-cantly higher than the 0.02e0.03 kg CO2 equivalents per NOK foundin most service industries, including the more general education IOsector. The CF of NTNU alone is in the same range as the CF calcu-lated for all municipal public services and administration inTrondheim, estimated to be approximately 95 thousand tons inLarsen and Hertwich (2009).

3.2. Structure of CF

The main aim of the work was to investigate the structure of theCF. Already having a detailed overview of energy use, waste andsome transport/travel, NTNU was eager to investigate the othercontributors to the CF. In Fig. 1 we illustrate the main contributingelements to the CF. The main categories in Fig. 1 are based on morethan 200 financial account entities of NTNU (full detail on CFcontributing elements are given in Appendix B). The results showthat the CF is distributed fairly equally among the aggregatedcategories, with Energy (electricity assumed to be produced witha Nordic mix at 189 g per kWh), Buildings, and Equipment eachcontribute to 19 percent of the CF. Further contributions includeTravel (16%), Consumables (11%) and Services (5%).

Large parts of the Energy and Building CF are related to largesingle contributions, such as electricity and district heatingsupplied to all buildings. Conversely, Travel, the Equipment, andConsumables CF constitute awide range of contributions that spansthe different faculties/institutes of the University. In Table 3 this isindicated in ranking the highest constituents of the CF of NTNUdivided into the responsible unit (faculty/department e> Institute/section). The five highest contributions are all connected to theFinances and Properties department relating to Energy andBuildings.

The table further reveals other interesting findings: largecontributions from publications purchased by the library, scientific-technical equipment and lab supplies for the faculty of Medicine,and also traveling of students connected to the Internationalsection constituting the single highest contribution to the CF ofNTNU travels. More detail on the CF of different segments of NTNUwill be provided in the following section.

3.3. Per department CF

The financial accounts of NTNU used as data for the EEIOmodeling performed in this paper enable a standardized

19%

16%

19%

19%

11%

5%

10%

Energy

Consumables

Other

Equipment

Buildings

Travles

Services

on of the CF of NTNU.

Table 3Highest contributing elements in the CF of NTNU.

Purchase Type CF Responsibility Faculty/department Institute/section

1 Electricity Energy 13216 Property Finances and properties El-Tech2 District heating Energy 4268 Property Finances and properties Heat, ventilation, sanitation3 Joint services, buildings Buildings 2227 Property Finances and properties Property management, staff4 Renting of premises Buildings 2172 Property Finances and properties Property management, staff5 Construction work Buildings 1974 Property Finances and properties Property management, project6 Publications Consumables 1476 Administration Study department Library, UBIT7 Sci./Tech. equip.,e Equipment 1334 Study programme Medicine Faculty administration8 Sci./Tech. equip., 8 y Equipment 1050 Study programme Medicine Faculty administration9 Construction work Buildings 1006 Property Finances and properties Property management, staff10 Sci./Tech. equip., 12 y Equipment 982 Study programme Medicine Faculty administration11Research services Services 884 Study programme Engineering SFI (petroleum sector)12 Sci./Tech. equip., 8 y Equipment 804 Study programs Natural science Faculty administration13 Heat, vent, sanit Buildings 763 Property Finances and properties Property management, project14 Travel, students Travels 736 Administration Study department International section15 Installations, misc Buildings 667 Property Finances and properties Property management, project16 Lab supplies Consumables 592 Study programme Medicine Cancer molecular medicine17 Audio-visual equip. Equipment 529 Administration Study department Support systems18 Chemicals Consumables 517 Property Finances and properties Property management, project19 Serving of food Consumables 506 Administration Study department Post-education20 Lab supplies Consumables 497 Study programme Medicine Neurology

H.N. Larsen et al. / Journal of Cleaner Production 48 (2013) 39e4742

segmentation of the CF divided into both faculty/departments andinstitute/section. In Fig. 2 we summarize using three main areas ofresponsibility:

Property: all activities connected to the running of buildings:energy, construction work, maintenance, cleaning, etc.

Administrative (Adm.): Common administrative services:library, student services, student admission section, student orga-nizations, etc.

< 1%19%

10%

16%21%

13%

20%

Prop38Adm.

15%

Study programm

47%

Energy (E) Travels (T) Buildings (B) Equipme

0 2000 4000

ArchitectureHumanities

IMEEngineering

MedicineNatural ScienceSocial Sciences

Carbon Footprint [to

TT

TT

T

T

BB

B

EqEq

EqEq

Eq

C

C

S

O

BT

C

S

Eq

T O

B

OCEqB

Fig. 2. Per-department compo

Study Programmes: all faculties: Social Science, Natural Science,Medicine, Engineering, Information Technology, Mathematics andElectrical Engineering (IME), Humanities and Architecture.

As previously indicated, the CF related to property constitutesmainly GHG emissions connected to the purchase of energy andother building related activities. For the common administrativeservices provided, the CF is more complex with contributions fromall elements, except Energy. Consumables are ranked highest with

50%

< 1%

40%

2%3%1%3%

erty %

es

nt (Eq) Consumables (C) Services (S) Other (O)

6000 8000 10000 12000nnes of CO2 equivalents]

CC

C

SS

S

OO

O

E B

sition of the CF of NTNU.

Per student Per 60 study-points Per employee Per publication point Per million NOK 0

5

10

15

20

25

30

35

40

45

50

55

O

C

f o s e n n o t [ t n i r p t o o F

n o b r a C

2

] s t n e l a v i u q e

Architecture Humanities IME Engineering Medicine Natural Science Social Science

Fig. 3. Normalized CF of different faculties.

3 http://sydney.edu.au/strategic_planning/information/enrol_index.php.

H.N. Larsen et al. / Journal of Cleaner Production 48 (2013) 39e47 43

a contribution of 21 percent, much of which is due to publications/books for the library and also food services at meetings etc. The CFconnected directly to the different study programs (faculties)contributes to 47 percent of the total CF of the university. In Fig. 2we illustrate the CF per faculty, also divided into the contributingelements. Results show large variations, both in the total CF and inits structure. To further compare the CF of the different faculties, wehave to normalize the result. This is illustrated in Fig. 3. The chosennormalization factors are: the number of students registered, 60study points, equivalent to one year of education, employees,publication points (Norwegian system; on average 1.2 points perscientific publication) and per million NOK of expenditure.

The results show large variations in the normalized CF. The perstudent CFs varies from 0.58 tons per student in Social Science toalmost 10.8 tons per student in the Faculty of Medicine, a factor of19 in difference. Relating the CF to the equivalent of one year ofeducation, 60 study points, eliminate factors such as drop-out ratesand differences in the average number of study points per studentper department, still shows a factor of 15 in difference. Since somefaculties have a higher focus on research, we also normalize the CFto the number of employees and per publication point. The resultsshow similar structure, although differences are less in magnitude,compared to per student CFs. Humanities and Social Science rankslow in both cases, while the Faculty of Medicine again ranks thehighest, but now much more closely followed by the Engineeringand Natural Science faculties regarding CF per employee, and theFaculty of Architecture regarding CF per publication points.

When the CF is normalized per NOKs of expenditure, results arequite different. Note the expenditures only consider those contrib-uting to the CF, thus excluding expenses for salaries, etc. Resultsshow quite similar CF intensities across different faculties. Normal-izing by the economic value of the purchases identify the Faculty ofMedicinewith the second lowest CF intensity. Reasons for this couldbe that the Faculty of Medicine has a fairly low fraction of CF relatedto travel, with a higher CF intensity compared to e.g. most types ofequipment. Also, the CF related to equipment has a slightly cleanerintensity, in terms of CF per NOK, for the Faculty of Medicine. This isbecause a fraction of the equipment purchased is specialized labo-ratory equipment with a lower CF per economic value in our model.A fairlyhigh fractionof consumables, suchas chemicals andother lab

supplies, gives the Faculty of Natural Science the highest intensity oftheir purchases made. However, the small variations of intensitiesindicate that it is the volume, and not the type, of purchases that isthe main reason for the difference in CF with other normalizationfactors across different faculties.

4. Discussions

4.1. Normalization and comparing results

Normalization of the CF per study programme (faculty) identifieslarge variations. The results clearly show that to either educatea student or produce a publication on average causesmuch less GHGemissions in the Faculty of Humanities and the Faculty of SocialScience, in comparison to themore engineering and science orientedstudy programmes. A low contribution of transport in the Faculty ofHumanities and equipment in the Faculty of Social Science is espe-cially apparent. Inmost cases, differences innormalizedCFs are likelydue to of the inherent nature of thefield of study; studyingmedicinerequires specific high quality equipment. Other differences could bedue tomore cultural factors in the different departments, e.g. relatedto the eagerness of traveling to conferences. In some cases, evensingle influential individuals can make important decisions that canheavily influence the CF. This is especially likely to be the case in thefinances and property department, where a few people managea substantial part of the CF. Nevertheless, normalized CFs perdepartment/faculty will identify the largest contributions to the CFand thereby allow department/faculty-specificmitigation strategies.

Per-faculty calculations are also available in the EEIO modelingfor the university of Sydney (Baboulet and Lenzen, 2010). Using thetotal material requirements (TMR) indicator, it identifies experi-mental research such as science, veterinary science, and agricultureto have much higher material intensities in contrast to economics,law, art and education that have a lowmaterial intensity. In Baboulet(2009), we also find GHG emission inventory of the different facul-ties. In both cases, results are normalized per $ only. However, per-faculty enrollment data are easily available3 and enable us to do

H.N. Larsen et al. / Journal of Cleaner Production 48 (2013) 39e4744

a more direct comparison. Normalizing the results of Baboulet forfour selected faculties comparable to NTNU faculties, show a CF ofapproximately 9.6 tonnes per student for the Faculty of Medicine,4.2 tonnes per student for the Faculty of Science, 1.7 tonnes perstudent for the Faculty of Architecture and0.7 tonnesper student forthe faculty of Economy (economic studies are at NTNU allocated tothe faculty of Social Science). These numbers indicate a remarkablysimilar structure in the CF per student indicator across differentfaculties at the University of Sydney compared to NTNU. It supportsthe theory that faculty-specific CFs are related to the nature of thestudies provided. Note that in the Baboulet study, the CF of energyuse is allocated to the different faculties and that the NTNU studyincludes the CF from investments.

In many CF inventories of universities, only selected Scope 3contributions are included. This is also the case in Klein-Banai et al.(2010) in the work on the University of Illinois at Chicago (UIC).Comparing this to the NTNU results are therefore not straightforward. Despite only including selected scope 3 emissions, thework of Klein-Banai et. al interestingly enough indicate a CF per fulltime equivalent enrollment of 8.8 tonnes, approximately twice thenormalized NTNU CF. We assume the reason for this to be a muchhigher emission intensities in energy use, and the fact that the UICwork also includes commuting of both staff and students.

4.2. Actions to reduce CF

Results from the EEIO modeling show that the CF of NTNU iscomplex with contributing elements from a wide range of activi-ties/purchases: energy, travel, buildings, equipment, consumablesand services. The different contributions require different actions.The CF related to energy and buildings are mainly the responsibilityof the property management. New university buildings shouldclearly apply GHG life cycle accounting at an early stage to mini-mize the CF of construction, maintenance and management ofbuildings and energy use throughout the lifetime of the building. Inexisting buildings actions can also be taken, regarding for exampleopening hours (NTNU has an 24 h open policy in all buildings),more sophisticated control of heat, ventilation and lighting, and theevaluation of different energy sources (NTNU is currently evalu-ating heat-pumps versus district heating at selected locations). Tosome degree the users (student and employees of the differentfaculties e institutes) also have the possibility to influence the CF,for example to turn off lights in unoccupied offices, avoid unnec-essary standby modes of office computers and machinery, andminimizing the need for building related services such as cleaning.

Most faculties and joint administration departments relyheavily on travel. Most of this is air travel with high emissionintensities. The introduction of video conferencing is one promisingway to reduce the need for travel. At NTNU, video conferencingequipment is made available in several locations ranging incapacity from 10 to 150 people. Clearly, this could especially bea good option for meetings and guest lectures. The CFs of equip-ment and consumables together form the largest contributors tothe CF of NTNU. The Faculty of Medicine and the Faculty of NaturalScience have especially high contributions in these categories. Insome cases, such as the purchase of office computers, food, andcopy paper, environmental information, such as Life Cycle Assess-ments (LCAs) and Environmental Product Declarations (EPDs), ofconsumer products is becoming more widespread and can beapplied in green purchasing strategies. We do not expect environ-mental information to be widely available at a more granular levelfor more specific scientific equipment and supplies. Actions herecould instead focus more on the actual use of products to ensure tomaximize lifetime on products and minimize use and wastegeneration of consumables.

4.3. Strengths and weaknesses of model

While effective in providing a complete overview of the total CFcovering all indirect GHG emissions, EEIO modeling has certainweaknesses that need to be addressed. Most important is the lackof detail which renders it difficult to capture changes in theprocurement pattern. Although it is possible to differentiatebetween things like medical precision equipment versus othermachinery and land transport versus water transport, the detail ofthe IO sectors (Appendix A) is not sufficient to separate betweendifferent types of specific equipment, such as office computers.

Secondly, using EEIO modeling, we usually assume a fixedlinear relationship between emissions and economic spending,and thereby do not capture potential economy-of-scale effects andalso leave the model vulnerable to other price issue variations(such as currency fluctuations, tax level changes on specificproducts) within the different IO sectors. Combining the weak-nesses of both detail level and price issues introduces potentiallysevere problems of an EEIO inventory. For example if the univer-sity decides to purchase more environmental friendly computersat a higher price, the EEIO model will identify this as an actionincreasing the CF.

To deal with these limitations, further development of the EEIOmodel is necessary. One solution is to increase the hybridization(Heijungs and Suh, 2002; Suh and Huppes, 2005) of the model byusing data from LCA databases to cover not only Scope 1 and 2, butalso significant Scope 3 contributions. Another possibility is todevelop a set of indicators based on the most important contrib-uting elements found in the EEIO calculations, for example thenumber of flights taken by employees, kWh per m2, liters of fuelfor transportation, or fraction of purchases covered by environ-mental requirements. This solution is easier to implement as itallows for establishing indicators not directly connected to theEEIO framework, however, it is also less methodologicallyconsistent.

5. Conclusions

To summarize, a university is a complex enterprise with a widevariety of contributions to the CF. EEIO modeling has proveneffective in identifying both target areas and differences in the CFacross different faculties/departments. The high contribution ofindirect, scope 3 emissions clearly shows the need of including thissegment of the inventory in order to develop a complete CF. Thestudy of NTNU shows that the scope 3 contributions cover a quitelarge set of elements, and that EEIO modeling combined withfinancial data from the account system has proven a possiblesolution for including these. Furthermore, dividing CF into facultiesusing EEIOmethodology enables us to compare universities aroundthe world more consistently. Using complete multi-regional EEIOmodels matched to a set of universities in investigating this couldbe an interesting possibility in further work.

However, when implementing mitigation actions, more specificdata are necessary to sufficiently track the effect. This is possibleeither through hybridizing the EEIO model or through the estab-lishment of a set of indicators on the environmental performance ofthe different areas, as previously indicated. Although indicatorswould be an easy way to measure some key developments in themitigation strategy, only an EEIO model would capture thecomplete CF in avoiding cut-offs and rebound effects.

The work with NTNU was followed by a similar analysis of thestudent organization of NTNU (SiT). This includes student villages,training centers, kindergartens, canteens, etc. Further work couldtherefore also aim to link these calculations to cover a larger part ofthe CF of a student’s life.

H.N. Larsen et al. / Journal of Cleaner Production 48 (2013) 39e47 45

Acknowledgments

We would like to thank three anonymous reviewers andChristine Hung for their valuable comments and suggestions.

Appendix A

Table 4NACE industry classification.

NACE Industries NACE Industries

Y01 Agriculture, hunting and related service activities Y36 Manufacture of furniture; manufacturing n.e.c.Y02 Forestry, logging and related service activities Y37 RecyclingY05 Fishing, operating of fish hatcheries and fish farms Y40 Electricity, gas, steam and hot water supplyY10 Mining of coal and lignite; extraction of peat Y41 Collection, purification and distribution of waterY11 Extraction of crude petroleum and natural gas Y45 ConstructionY12 Mining of uranium and thorium ores Y50 Sale, maintenance and repair of motor vehicles and fuelY13 Mining of metal ores Y51 Wholesale trade and commission tradeY14 Other mining and quarrying Y52 Retail tradeY15 Manufacture of food products and beverages Y55 Hotels and restaurantsY16 Manufacture of tobacco products Y60 Land transport; transport via pipelinesY17 Manufacture of textiles Y61 Water transportY18 Manufacture of wearing apparel Y62 Air transportY19 Tanning and dressing of leather Y63 Supporting and auxiliary transport activitiesY20 Manufacture of wood and of products of wood and cork Y64 Post and telecommunicationsY21 Manufacture of pulp, paper and paper products Y65 Financial intermediationY22 Publishing, printing and reproduction of recorded media Y66 Insurance and pension fundingY23 Manufacture of coke, refined petroleum products Y67 Activities auxiliary to financial intermediationY24 Manufacture of chemicals and chemical products Y70 Real estate activitiesY25 Manufacture of rubber and plastic products Y71 Renting of machinery and equipmentY26 Manufacture of other non-metallic mineral products Y72 Computer and related activitiesY27 Manufacture of basic metals Y73 Research and developmentY28 Manufacture of fabricated metal products Y74 Other business activitiesY29 Manufacture of machinery and equipment n.e.c. Y75 Public administration and defenceY30 Manufacture of office machinery and computers Y80 EducationY31 Manufacture of electrical machinery and apparatus n.e.c. Y85 Health and social workY32 Manufacture of radio, television and com. equip. Y90 Sewage and refuse disposal, sanitation and similar act.Y33 Manufacture of medical, precision and optical equip. Y91 Activities of membership organisation n.e.c.Y34 Manufacture of motor vehicles, trailers and semi-trailers Y92 Recreational, cultural and sporting activitiesY35 Manufacture of other transport equipment Y93 Other service activities

Appendix B

Table 5

NTNU accounting CF contributing elements.

Category CF Category CF Category CF

Electricity 13298 Management buildings, gen. 275 Cleaning services 36Travels employees, filing req. (f) 6744 Travels external lectures, f 269 Subsidy student arrangements 35District heating 4268 Car allowance 258 Painting 33Construction work 3334 Operation of cars and mach. 254 Maintenance - elevators 32Lab supplies 2942 Operation materials 237 Subsidy, internal 32Tech.-Sci. Equip. 8 y 2762 SD-equipment 221 Relocation expenses, nf 30Renting premises 2458 Advertising 220 Services, computer equipment 30R&D services 2316 Travels sensors, nf. 210 Office machines 29Joint-exp. premises 2277 Membership fees 208 Maintenance computer prog. 29Travels employees, no filing (nf) 2228 Computer services 207 Renting means of transport. 29Consultancy services 2147 Maintenance e pipes 190 Teaching equipments, rent 29Computer equipment 1843 Maintenance e heat/vent 189 Central control 29Serving at meetings etc. 1749 Other expenses, travels 187 Accessories - cleaning 26Publications 1635 Telecommunications 178 EU-projects 24Tech.-Sci. Equip. 1597 Copying expenses 175 Guard duty 22Other services 1584 Renting accommodation 170 Service teaching equipment 21Chemicals 1554 Traveling fieldwork, f. 169 Telecommunication, line 19Food, travels 1465 Marketing activities 165 Representation expenses 19Travels students, non filing 1402 Machinery and tools 163 Service furniture etc. 17Tech.-Sci. Equip. 12 y 1304 Travels sensors, f. 163 Other teaching supplies 17Heat, sanitation, ventilation, HSV 1197 Student-travels, congress, nf. 162 Catering expenses 16Travels external lectures, nf 1092 Maintenance e paint 151 HSV equipment 16

(continued on next page)

Table 5 (continued )

Category CF Category CF Category CF

Student-travels, congress, f. 1051 Machinery and tools,10 y 143 Other freight expenses 15Office supplies 993 Travel expenses deductable 140 Travels foreign students, nf 15Building projects 882 Computer programmes 138 Gifts, external 15Maintenance buildings 876 Travels students, f. 138 Resale 13Audio-visual equipment 862 Maintenance - materials 135 Relocation expenses, f 12Electrics buildings 771 Subsidy student org. 135 Accessories e el. 11Electric works 759 Security installations 128 Audio-visual accessories 10Other course expenses 713 Advisory services 127 Management e pipes 10Other installations 697 Renting communicationline 125 Office machinery, 3 y 10Misc. expenses 678 Property taxation 111 Research vessel, renting 9Consumables 666 Telecommunication equip. 106 Courses, external 8Freight and transport 662 Other renting expenses 105 Computer equipment, renting 7Lab equipment 661 Mainten., Tech-Sci. equipment 105 Services telecommunication 7Forwarding of goods 650 Computer software 101 Ventilation 7Course expenses 605 Services office machinery 98 Travel expenses, teaching 6Furniture, etc. 595 Mainten. mat. e pipes 96 Food travels, nf. 6Andre driftstilskudd 506 Mainten. mat. e heat/vent. 86 Services NTNU, TTO 6Feeding 484 Management e heat/vent 79 Travel expenses, hiring, f. 5Tech.-Sci. Equip., 4 y 468 Canteen equipment 72 Foundation work 5Other equipment 466 Maintenance, machinery 70 Other maintenance, machinery 5Office machines, renting 465 Services, tech-sci. equipment 65 Gas, welding etc. 5Printed matter 453 Outdoor works 64 Machinery tools, renting 5Gases 451 Photo supplies 64 Other public taxations 4Maintenance - construction 438 Paint works 62 Other expenses, vehicles 4Data accessories 431 Tech.-sci. equipment, renting 61 Vehicles, purchase 4Service, data programmes 430 Telecommunication equipment 61 Automatize 4Office services 417 Vehicles, 7 y 60 Resale, nf. 4Technical consumables 408 Property taxation 57 Taxation of cars and machines 4Teaching services, 396 Teaching equipment 57 Bank fees 4Construction work 392 Service, other equipment 54 Accounting services TTO 3Books 379 Maintenance mat. - buildings 54 Outdoor works 3Furniture, etc., 10 y 379 Plants, seeds, etc. 52 Audit services 3Mobile phone 373 Gifts 51 Furniture, etc., renting 3Postage 363 Subsidy for students 51 Other posting expenses 3Construction counseling ser. 349 Service, vehicles 50 Traveling, administration 2Refurbishing works 326 Travels field work, nf. 46 Patent expenses 2Newspaper, journals, etc. 319 Working clothes 46 Copying fees 2Waste treatment 298 Fire protection 45 Other insurance expenses 1Other management, premises 295 Machinery and tools, 5 y 44 Losses on accounts receivable 1Copy paper 291 Announcements 43 Patient fee 1Operations, research vessel 285 Mainten. mat. e painting 39 Travel expense, examinations 1Heating oil 281 Service, research vessel 36 Total 92.1k

H.N. Larsen et al. / Journal of Cleaner Production 48 (2013) 39e4746

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