Carbon footprint of a university in a multiregional model: the case of the University of Castilla-La Mancha

Accepted Manuscript Carbon footprint of a university in a multiregional model: the case of the University of Castilla-La Mancha Nuria Gómez, María-Ángeles Cadarso, Fabio Monsalve PII:

S0959-6526(16)30677-1

DOI:

10.1016/j.jclepro.2016.06.009

Reference:

JCLP 7380

To appear in:

Journal of Cleaner Production

Received Date: 31 March 2015 Revised Date:

23 May 2016

Accepted Date: 1 June 2016

Please cite this article as: Gómez N, Cadarso M-E, Monsalve F, Carbon footprint of a university in a multiregional model: the case of the University of Castilla-La Mancha, Journal of Cleaner Production (2016), doi: 10.1016/j.jclepro.2016.06.009. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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Carbon footprint of a university in a multiregional model: the case of the University of Castilla-La Mancha Gómez, Nuria, Cadarso, María-Ángeles, Monsalve, Fabio*

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Universidad de Castilla-La Mancha Facultad de Ciencias Económicas y Empresariales de Albacete Plaza Universidad, 2 Phone: +34967599200 . Fax: +34967599216. E-mail: [email protected]*, [email protected], [email protected],

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*Corresponding author HIGHLIGHTS

A University carbon footprint is measured by a hybrid environmentally extended MRIO Twofold Hybridization: uses regional and international less aggregated coefficients The analysis compares the effect of applied abatement measures to alternative ones The 9 years period shows the evolution of regional and sectoral origin of CO2e Results highlight the relevance of energy generated emissions, directly and embodied

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ABSTRACT

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The increasing concern about the environmental performance and sustainability of firms and organizations also involves educational institutions. If universities aim to become leaders in sustainability aspects, they must adopt strategies that involve the entire university system. As a useful tool for this purpose, the objective of this study was to calculate the carbon footprint for the University of Castilla-La Mancha for the period 2005-2013. The calculation of the university carbon footprint was accomplished through a hybrid environmentally extended input-output model in a multiregional framework, which constitutes a novelty of the analysis. The proposed tool also allows the calculation of the potential for emissions reduction of abatement measures. The carbon footprint induced by the consumption of university employees’ wages is also undertaken as a way to increase awareness and university outreach to society. The results show the relevance of both imports and indirect emissions and highlight the significance of energy-related emissions enabled by sectors such as renting, electrical and optical equipment, manufacturing or even services.

KEYWORDS University carbon footprint, Consumer responsibility, Input-output, Procurement emissions.

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ACCEPTED MANUSCRIPT 1. Introduction

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Universities are educational institutions that should be among the leaders of the environmental and sustainability movements. Concerns about environmental responsibility require quick changes in awareness and consumption patterns to reduce emissions, and universities should play a major role in promoting, informing/assessing and supporting such changes. After the “Brundlandt Report” (WCED, 1987) and Rio Conference, universities are increasingly committed to the environment and sustainable development. As a proof of that commitment, in 1993, more than 200 European universities from the Conference of European Rectors (CRE) signed The University Charter for Sustainable Development in Barcelona (COPERNICUS, 1994). Since then, the number of declarations, charters and partnerships have been increasing in number and relevance, achieving multiple commitments (Lozano et al., 2013b). In Spain, the Assembly of the Conference of Spanish University Rectors (CRUE) approved the creation of a working group to foster environmental management and the inclusion of environmental issues and awareness throughout the higher education community. Hence, in 2002 the CADEP (Environmental Quality, Sustainable Development and Risk Prevention Commission) started operating.

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These works set the guidelines; however, because of the slow rate of change of the universities (Lozano et al., 2013a) or because the changes needed require an integrated, crossdisciplinary point of view (Cortese, 2003), there is room for improvement, and sustainable development is still an innovative idea in the university context (Lozano et al., 2013b). Even more, we can consider that it is not a high priority for higher education at least in the European context because there has only been reference to sustainability in relation to financial support in the section of declarations and policy positions of the European Universities Association (EUA, 2014). To be effective, the inclusion of sustainability in higher education must spread throughout the entire university system, including education-teaching (courses and curricula), research, and external community (community outreach) because all of these aspects are interlinked and interconnected (Cortese, 2003; Ferrer-Balas et al., 2010). Moreover, if universities wish to become leaders and to educate leaders in sustainability, they should practice it and include sustainability issues in their day-to-day operation, planning, investment, purchasing and so on and need instruments to report and assess their actions and achievements. The concept of footprint (Hoekstra and Wiedmann, 2014) as a global measure of an activity, company or geographical area performance regarding the different spheres of sustainability (Foran et al., 2005; Kucukvar et al., 2014; Wiedmann et al., 2009) and more specifically the carbon footprint (CF) regarding the environment could be a perfect tool for this purpose (Lambrechts and Van Liedekerke, 2014). Consequently, the aim of the present paper was to contribute to the challenge of leadership in environmental sustainability providing the calculation of the CF of the University of Castilla-La Mancha (UCLM) in Spain using a hybrid multiregional input output model. The performed calculation was then used to assess the impact of the energy-saving measures introduced by the university and as a baseline scenario to consider how other policies could modify the outcome of the emissions. The counterfactual is built on easily affordable measures and could provide lessons for other educational institutions. Literature on universities and other education institution environmental responsibility has been prolific, reflecting the concerns on this issue of the academia; however, there is room for improvement regarding its application to the institutions themselves, as we have previously noted. Integrating sustainability in educational programs has enormous potential for the future, (e.g., special volumes by the 2

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Journal of Cleaner Production (Adomßent et al., 2014; Lozano et al., 2015; Wang et al., 2013)); however, it must be accompanied by the university’s assessment of its own footprint as an institution. The two most widespread measures calculated by previous literature are the ecological footprint (Conway et al., 2008; Gottlieb et al., 2012; Klein-Banai and Theis, 2011; Lambrechts and Van Liedekerke, 2014) and the carbon footprint (Alvarez et al., 2014; Güereca et al., 2013; Larsen et al., 2013; Ozawa-Meida et al., 2013; Thurston and Eckelman, 2011; Yazdani et al., 2013). Focusing on carbon footprint literature, process based life cycle assessment analysis (P-LCA) coexist with environmentally extended input-output analysis (EEIOA) and some cases hybridize both methodologies ((Achten et al., 2013; Alvarez et al., 2014; Güereca et al., 2013; Ozawa-Meida et al., 2013; Thurston and Eckelman, 2011)).

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Regarding our analysis, the model used implies two main improvements: the multiregional context, which is used for the first time to our knowledge in university carbon footprint calculations, and the hybrid character of the model. The multiregional context avoids making assumptions about production technology and related emissions by considering the technology of production and emissions for each country and, in doing so, providing accurate estimations. The same technology assumption is unavoidable in single-region models (see (Ozawa-Meida et al., 2013) or (Thurston and Eckelman, 2011)), and the assumption of all imports being produced using the same technology, however different to the domestic one, is also considered (Larsen et al., 2013). The hybrid model combines the completeness of EEIOA, which avoids the truncation errors of pure P-LCA which results in systematic underestimation of the environmental load, with the accuracy of P-LCA by collecting more detailed information at some important points of the analysed activity or process (Lenzen and Dey, 2000; Suh, 2004; Suh et al., 2004; Weinzettel et al., 2014; Zafrilla et al., 2014).

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The hybrid property of the model consists of the inclusion of more specific information in two different ways: first, by including regional input-output information on technical coefficients for both, the procurement expenditures of the education sector and distinguishing their domestic and imported origin when available, and second, by enlarging the number of sectors considered at the initial stages of production to minimize the aggregation problem (Lenzen, 2011). The completeness mentioned of the EEIOA allows emissions from the entire supply chain of an organization to be captured; as a result, it is capable of providing a full carbon footprint estimation, including scope 3. In doing so, the CF accounts for not only on-site emissions (scope 1) and emissions related to the electricity purchased by the organization (scope 2) but also all other indirect emissions involved (the already mentioned scope 3) (WRI and WBCSD, 2004). Moreover, even considering a partial inclusion of scope 3 emissions (a cutoff threshold), the P-LCA method alone cannot fulfil the minimum requirements set by the main standards of footprinting or life cycle accounts developed (Huang et al., 2009). The model also acknowledges the uncertainty that could arise from several sources. A Monte Carlo analysis has been performed to understand the implications for the final emissions when the model parameters are perturbed. The results are consistent with previous literature on the topic. The most interesting conclusions that can be drawn from the environmental performance of an institution orientate mitigation keys to detail how changes in procurement policies can improve institutional footprints together with simple rules on energy and reducing the materials requirement (Achten et al., 2013; Baboulet and Lenzen, 2010). We do not have detailed information on the environmental issues of university providers, but we offer an alternative proposal that calculates how real changes in university expenditures have affected 3

ACCEPTED MANUSCRIPT the generated emissions, instead of calculating how changes in providers or input requirements would change carbon emissions, and also how other alternative arrangements would work in practice.

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Because we can define the CF as the total, direct and indirect, carbon emissions required to satisfy a given consumption (Minx et al., 2009), we compute the cradle-to-gate environmental load of UCLM activity using the basic information of expenditure provided by the university budget. The EEIOA is one of the best methods to address this financial information (Baboulet and Lenzen, 2010; Larsen et al., 2013; Larsen et al., 2012)). This use has the advantages more easily understood results for financial and other company stakeholders, simplifying the identification of possible paths for reduction and hotspots while offering more abatement options, increasing the awareness of people involved and facilitating the screening assessment (Huang et al., 2009). The drawbacks are related to the use of monetary data assuming price and quantity proportionality, a problem shared with the majority of the CF calculation using input-output models (see for instance, related to environmental impacts (Merciai and Heijungs, 2014)). However, the uncertainties of EEIOA are often lower than the truncation errors of P-LCA (Lenzen, 2001) and that the latter cannot be properly used to assess the impacts of services (Junnila, 2006), as is the case of a university.

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A case study was performed on the UCLM, a regional multi-campus institution that can be considered a medium-sized Spanish public university with over 30,000 registered students in 2012 and over 3,000 workers including researchers, teachers and administrative staff. These figures rank the UCLM fifteenth in terms of students and approximately 20th in terms of workers and budget out of fifty Spanish public universities (Ministry of Education Culture and Sport (Spain), 2015). It is located in Castilla-La Mancha, a region in the centre of Spain, and is the only university in the region. It was established in the 1980s, and since then, it has been the key to regional development. For instance, the UCLM has been responsible for more than a quarter of total regional growth in the last two decades, and the regional income would be 19% less without the contribution of the UCLM (Pastor and Peraita, 2010). These are positive externalities of the UCLM, but what about the negative ones? In this paper, we address this missing issue about negative externalities in the previously cited report by measuring the carbon footprint of the UCLM.

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The UCLM does not have a specific footprint reduction plan; however, an energy-saving plan was applied in 2011, 2012 and 2013, which we could expect to reduce emissions (UCLM (University of Castilla-La Mancha), 2013). This is the common profile of Spanish universities, where environmental sustainability policies have not been extensively implemented, and reduction in energy consumption is the one most implemented (Larrán Jorge et al., 2014). The existence of strong expenditure restrictions in the present context has led to the promotion of conservation tools based on optimising the use of already-installed infrastructures at zero cost with no investment requirements. On the other hand, the current awareness about the relevance teaching and research on energy and environmental issues has resulted in the International Excellence Technological and Scientific Campus on Energy and Environment (Cytema). The recent economic crisis has helped to reduce emissions levels; however, these emissions reductions are not proven to continue over time (Zafrilla et al., 2012). As is the case for the other economic agents, Spanish universities have been affected by the economic crisis such that total expenditures have been reduced generally by 14% between 2009 and 2012 and by 38% specifically in the UCLM (MECD, 2015). This situation has led to a reduction in related 4

ACCEPTED MANUSCRIPT emissions; however, it is necessary to know whether this change has been due to expenditure rationalisation measures or whether emissions will recover the previous path as the university’s expenditure levels rebound. In this paper, we measure the emissions levels for the UCLM for the period 2005-2013, a period rather than a single point in time as is usual for literature on university footprints.

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The rest of the paper is organized as follows: section 2 describes the proposed method and the sources of data and data preparation, Sections 3 and 4 discuss the results, and Section 4 provides the conclusions. 2. Methods and data preparation

CF − Exp = eˆ ( I − A) −1 Uˆ = ε Uˆ

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We are interested in measuring the consumer responsibility, or carbon footprint, of the UCLM as an education service provider following the line of the literature on corporate carbon footprinting (Berners-Lee et al., 2011; Huang et al., 2009; Wiedmann et al., 2009) which has been widened to include the analysis of environmental performance of public institutions (Wiedmann and Barrett, 2011) and universities (Larsen et al., 2013; Lenzen et al., 2010). We start from the usual consumption-based inventory perspective to calculate the carbon footprint of the UCLM in a multiregional framework to avoid the use of domestic technology assumption in the production of imports and resulting emissions (Kanemoto et al., 2012; Peters, 2008) from the following expression (1): (1)

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where eˆ is a diagonal matrix of emission coefficients (emissions by unit produced by each sector of activity) at the national level for Spain and any other country and region from which Spain imports goods and services; I is the identity matrix; A is the technical coefficients matrix for the mentioned countries and regions, where the domestic intermediate coefficients (Arr) are in the main diagonal and the intermediate imports (exports) coefficients (Asr) are in the offdiagonal positions; ( I − A) −1 is the Leontief matrix; and the product eˆ( I − A) −1 provides the

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total emission intensity. Finally, Uˆ is the diagonal matrix of final demand from the institution (see for instance, (Joshi, 2000) in a general case or (Baboulet and Lenzen, 2010) for a university case). We work with diagonal matrices for both the emission coefficients and the final demand to have the results expressed in matrix form (and also it is expressed as matrix the multiregional emission multiplier, ε ). This allows for the analysis of the total emissions downstream or the consumer responsibility perspective (by columns) and upstream or producer responsibility perspective (by rows) (Meng et al., 2015; Skelton et al., 2011). Because we are estimating the CF of a university from the vector of its requirements for production, for the downstream (columns) analysis we account for the total emissions embodied in the inputs directly and indirectly used for the university input production, and for the upstream analysis we account for the emissions embodied in direct inputs used for the university generated by their production process. The final demand vector U for the UCLM was obtained from the university accountancy department that provided us with operational expenditure data grouped in 160 homogenous categories. As highlighted by (Larsen et al., 2013), the institutional accountancy framework is suitable for EEIOA modelling. As a result, equation (1) links the university expenditure data mapped to fit the input-output sectors with their respective sectorial emissions and technology. The UCLM budget classifies expenditures into three main groups (current 5

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Table 1. UCLM budget for 2005, 2010 and 2013.

Expenditure chapters

Expenditure in constant 2013 €

I. Personnel costs

119.681.917

II. Current assets and services

50.280.099

2010

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expenditures, capital investment and financial expenditures) divided into 9 different chapters. Personal expenditure is the most important item while the weight of investment is significantly modified over the period. Table 1 shows the university budget in general groups for 2005 and 2013 where all nine sub-groups are present. The emissions were calculated from a muchdisaggregated version of the UCLM budget that provides 121 expenditure groups for the more heterogeneous chapters 2 and 6 and 33 groups for the remaining chapters with more internal homogeneity. Moreover, by using university expenditure data, we accounted for travel expenditure paid by the university, such as intra-campuses trips for administrative staff or teachers, which are even more important because the UCLM has four main campuses placed in cities that are separated by distances that range from 80 to 325 kilometres. Unfortunately, trips made and paid by students for academic reasons were not considered due to the lack of data. In relation to construction expenditure in the university budget, we followed (Larsen et al., 2013) and used depreciation data according to the life expectancy of buildings; because they are durable goods, the emissions embodied in their construction must be redistributed along their useful life.

2013

Variation

%

Expenditure in constant 2013 €

%

Expenditure in constant 2013 €

%

55,43

146.878.394

55,61

129.817.331

71,53

8,47

23,29

54.435.410

20,61

33.270.724

18,33

-33,83

0,74

370.230

0,14

101.280

0,06

-93,68

1,55

4.150.159

1,57

3.100.899

1,71

-7,14

20052013

1.602.187

IV. Current transfers

3.339.148

VI. Real investments

31.219.031

14,46

49.783.536

18,85

7.605.161

4,19

-75,64

0

0,00

0

0,00

0

0,00

0,00

294.000

0,14

259.680

0,10

0

0,00

-100,00

9.483.378

4,39

8.224.282

3,11

7.601.000

4,19

-19,85

VII. Capital transfers VIII. Changes in financial assets IX. Changes in financial liabilities

215.899.760

264.101.691

181.496.395

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III. Financial expenditures

Source: Own calculations.

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Although this is a rough comparison, it is possible to observe that the expenditure distribution has not changed much over the beginning of the period, whereas total expenditure has noticeably changed for some items from 2010. The expenditure in absolute terms increased mainly from 2005 until 2010 for real investments (approximately 60%), current transfers (approximately 25%) and also in personnel expenditures (over 20%), whereas it decreased drastically for financial expenditures (over 75%). The stringent budget result of the public budget constraints during the period is well reflected in the university budget. The university has decreased its budget by 22% in real terms, a growth that accompanied increases in the number of students and available degrees and other academic qualifications. In 2010 there was a strong reduction of the university expenditure that affected all chapters with a major impact in real investment. This convulsive period is an interesting natural laboratory for analysing how the changes in institutional decisions can affect environmental performance. The input-output data used to develop the multiregional input-output model and the emissions data came from the WIOD data base (Timmer, 2012). We used the maximum disaggregation level allowed by this database: 40 countries and an aggregated region for the 6

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rest of the world and 35 sectors. The emissions are calculated as CO2 equivalents, including the three greenhouse gases available in WIOD data, CO2 (Carbon dioxide), N2O (Nitrous oxide) and CH4 (Methane), which were added using IPCC information on global warming potentials values (Intergovernmental Panel on Climate Change (IPCC), 2006). Because we are interested in following the UCLM CF over time, the measure is calculated for the period 2005-2013. Data availability explains some limitations of the analysis. Emissions data from the WIOD are only available until 2009 and technology until 2011; thus, we consider that both emissions and technology change slowly, and the last year of available data can be considered a good approximation for 2011, 2012 and 2013, correcting for changes in prices.

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To improve accuracy by approximating a bottom-up approach or P-LCA, we took advantage of more detailed CO2 coefficients that were available for some of the countries that play a strong role in Spanish educational purchases, moving from the disaggregation of 35 available WIOD sectors to 65 Eurostat sectors (EUROSTAT, Several Years) where specific information on paper, printing, pharmaceutical, information and communication technology, electrical equipment, water, and many service-specific coefficients are available, only mentioning those that were significant in UCLM expenditure. These coefficients, built on data provided by Eurostat, were available for 26 countries, and they add to a range between 95.2% of the total university purchases (lowest value in 2007) to 97.0% (highest value on 2009). These detailed data were available for the period 2008-2011. These specific coefficients enable the hybridization of direct emissions, those generated by university purchases, without including the emissions generated by intermediate inputs required to produce the goods and services purchased by the university, indirect emissions in IO terminology. This additional result will be compared with calculations with more aggregated coefficients. The first step of the hybridization is accomplished from the decomposition of the Leontief matrix in a power series as used previously by (Baboulet and Lenzen, 2010; Zafrilla et al., 2014), where each power of the technical coefficient matrix A indicates a layer of the production process as in equation (2): 
 −  = ̂  + +  +  + ⋯ 

(2)

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Because the U vector shows the direct inputs the UCLM requires to provide its services, the first layer (that of the identity matrix) yields the emissions directly embodied in the inputs used by the university. Then, we used the emissions from the more disaggregated calculations performed using 154 items for the budget and the 65 emission intensities from Eurostat.

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In general, the use of WIOD data means that we are using national averages regarding emissions and technology of sectors rather than the regional averages where the institution is located, which would be preferable. This procedure is common in the literature (see for instance, (Espinosa et al., 2014) and (Ozawa-Meida et al., 2013)), but we tried to use regional information where available. This was the case, for instance, for distinguishing between domestic and imported requirements in the expenditure budget of the university while the distribution of imports among countries was calculated considering the share of imported intermediate inputs in the Spanish education sector (column) of the WIOD input-output table. In addition, we also used the regional data in a second step of hybridization, replacing Spanish technology gathered in the technical coefficients by the regional ones but only in the second layer of production of expression (2) related to the inputs required to produce the inputs directly used by the university. The remaining layers maintained the Spanish technology because it is more likely that the following layers involved other Spanish regions, in which case the Spanish average would be more appropriate. All of the regional data comes from the regional input-output tables of Castilla-La Mancha provided by the regional government for 7

ACCEPTED MANUSCRIPT the years 2005, 2006, 2007 and 2008 (IES (Statistics Servicies of Castilla-La Mancha), Several Years). From 2008 to the end of the period the lack of additional data forced us to assume regional technology as constant. The aggregation levels and classification are different for the three data sources; thus, that a matching effort was required. 2.1. Carbon footprint of the University workers

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We acknowledge that a complete footprint measurement requires detailed information of the three emission scopes. Scope 3 shall include all downstream- and upstream-derived indirect emissions, as is guaranteed by IOA. However, a public institution such as a university addresses special characteristics to be considered. First, employees salary is the main item in the university’s budget, ranging from 55% to 70%, as shown in Table 1. The labour costs cannot be classified as the final demand directly but rather as the income provided to the university workers, who will use it for the acquisition of goods and services, generating emissions in a later stage. Those emissions from the salaries are not included in the CF of a company or institution because they are outside of its responsibility area. However, a whole line of IOA relies on closed models that make households endogenous, which is justified because the amount of their consumption depends on their income which depends on the output of each of the sectors. As a result, the impacts measured in a closed model include not only direct and indirect effects but also induced consumption (Miller and Blair, 2009). Similarly, we can argue that the emissions from university worker consumption depend on their income, which depends on the activity of the university. Second, the amount of wages and the consumption they allow are often included in impact analyses (see for instance, regarding universities (Bessette, 2003), and (Roessner et al., 2013)) and also as indicators of sustainability in social or economic fields (Kucukvar et al., 2014); thus, it might be conceivable to include them all in the environmental part. Finally, considering the CF induced by the consumption of university wages, following the input-output closed model terminology could be an approach to increase awareness and commitment of the staff to a more sustainable university and to induce changes in their behaviour both outside and inside the campus. Furthermore, it might be a tool for increasing university outreach and its ability to become a leader and a vehicle to transform the society into a more sustainable one. This objective requires changes in consumption behaviour in addition to increases in energy efficiency or moving forward to a low carbon economy. Hence, we consider the calculation of the emissions derived from salary consumption to be crucial to a global measure of the university’s footprint. As a result, we followed a type of partially closed input-output model by calculating the induced CF of UCLM employee wages and performed an analysis that matched better with the measurement in national accounts of the output of non-profit institutions (public Universities) as the sum of their expenses. To adequately measure consumption-related emissions, we based our analysis on articles on household carbon footprint, among them (Druckman and Jackson, 2009; Duarte et al., 2010). Thus, we used an expression equivalent to (1) but where the final demand vector considers the expenditure by sector coming from university wages (assuming an average rate of savings of 20%). The expenditure consumption of university employee families was distributed following the regional consumption pattern provided by the Households Budget Survey provided by the National Statistical Institute (INE in its Spanish acronym) which considered 116 consumption items (INE (National Institute of Statistics), Several Years) aggregated into 35 sectors. As a result, we considered the expenditure from wages for university employees to be distributed among different consumption groups of items in the same proportion as any other household 8

ACCEPTED MANUSCRIPT in the region. We can express the final equation for the induced UCLM wages Footprint (CFWag) as in expression (3):

CF − Wag = eˆ( I + A + A 2 + A3 + ...) Uˆ W

(3)

where Uˆ W shows the wage consumption by the university employees as a diagonal vector and

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where the only modified layer is the second one, as explained for expression (2) . We also used the proportions of imported products from the regional input-output tables provided by the regional government for the years 2005, 2006, 2007 and 2008 (IES (Statistics Servicies of Castilla-La Mancha), Several Years) to distinguish the domestic or imported origin of the consumption expenditure from wages.

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Finally, it should be noted that the aforementioned method -as any other indirect estimation of emissions- is surrounded by several sources of uncertainty, from baseline information to the own assumptions of every particular model. Focusing on the former, the baseline input-output information, a relevant strand of literature has assessed its reliability through different tools (Temurshoev, 2015), and Monte Carlo (MC) analysis was one of the most extended. The seminal work of (Bullard and Sebald, 1988) concluded that “that input data uncertainties combine or cancel one another in such a way as to hold error magnification to acceptable levels”. A crude analysis to test this statement in our baseline dataset was performed using Monte Carlo analysis assuming certain levels of inaccuracy and studying the consequences for the final emissions values. Specifically, we proceeded to run 10.000 MC simulations by randomly perturbing the WIOT Data considering 1) a uniform distribution of the errors, 2) an average common level of inaccuracy of 10% for economic data and 15% for CO2-equivalent emissions data (UNEP, 2012), and 3) a final demand vector restricted to the university budget. The final emissions deviations range between +/- 8.5% with an average level of inaccuracy of 1.22%. These results are consistent with the literature (Karstensen et al., 2015; Moran and Wood, 2014; Wilting, 2012) which also suggests that the variation in economic data may not be important for consumption-based estimates at the national level (Peters et al., 2012). The other main sources of uncertainties are the assumptions of the model, i.e., those adopted to perform the hybrid analysis and the sector aggregation to harmonize the UCLM budget, the regional input-output table and the WIOT.

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3. Results and discussion

Two main conclusions can be drawn from the analysis, as shown in Figure 1. First, the emissions from wages are as important as the emissions embodied in the operational expenditures of the budget. This means that the average employee is much more responsible for emissions through their private life than through their job at the university, which is congruent with the lower time spent at work but also with the non-carbon intensive characteristic of higher education. Second, an irregular pattern initiated a clear reduction tendency only after 2010. Focusing on the results shown in Figure 1, the expenditure CF measurement had its highest value for 2008, 36.4 CO2e kt, and its lowest value at the end of the period analysed, 22.9 kt in 2012. This evolution in time is highly linked to the evolution of the budget and the reductions caused by the economic crisis. These results indicate that the UCLM is responsible for a low percentage of the emissions of the region of Castilla-La Mancha, between 0.32% and 0.43% even when including the emissions from wage consumption. In a similar line, the UCLM is only 9

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responsible for 5.1-6.9% of the emissions originated by the residential and service sectors (Table 2). Regarding the direct CF by UCLM, the evolution is similar but lower than that of the region, as it could be anticipated. Results found approximately 16.5 CO2e tonnes for the UCLM, compared to 30 for the region; because higher education, as a service sector, it is not emission intensive. Moreover, we must consider that in the CF we included imports that were not included in the emissions of the Castilla-La Mancha region (the emissions generated inside the borders of the region) and that the region has an economy strongly based on the tertiary sector with a share of services closer to 70%. The peak of emissions per employee in the UCLM in 2011 was caused by the reduction of the workforce of nearly 10%.

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Figure 1. UCLM expenditure and wages and related carbon footprints.

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Note: Direct emissions-Exp (-Wag) are the emissions directly required for the production of UCLM operational expenditures (UCLM wage consumption). Source: Own calculations.

Regarding the comparisons with other carbon footprint methodologies, such as P-LCA, it is important to note that direct emissions (estimated here as the emissions resulting directly from the production process of inputs required for the university and the goods and services consumed by the employees), account for a low share of the total emissions, approximately 17-29% (Figure 1), with higher figures for emissions from wages. Scope 1 emissions (roughly estimated using the direct emission coefficients of the education sector) (Berners-Lee et al., 2011) are negligible (below 0.44% of the CF from expenditure). These figures indicate that the truncation error can be relevant in the estimations of CF for universities or other similar institutions or small business, and the results can suffer from a significant underestimation. Comparisons with previous literature are limited, mainly because of the differences in methods. (Larsen et al., 2013) calculated for the Norwegian NTNU in 2009 a global result of 92 CO2 kt, 0.39 CO2e per purchased euro or 4.6 CO2 tonnes per student and 16.7 per employee. 10

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The figures of the UCLM analysis show much lower emissions per student and similar emissions per worker; this is mainly explained by the marked differences in the studentemployee ratio between the two institutions (approximately 3.7 for NTNU compared with 9.3 for UCLM). The UCLM CF for the same year is 33.2 kt; although the comparison can indicate that the UCLM performs environmentally better, their ecoefficency is the same. However, we consider Larsen et al.’s measurement to be infra-estimated because they considered all imported goods and services to have been produced with German technology, which is a very optimistic assumption when the German eco-efficiency coefficients are compared with others. For example, the World Bank eco-efficiency indicators for 2009, CO2 Kg/PPP GDP, provides a figure of 0.25 for Germany, 0.36 for Thailand, 0.71 for China or 0.37 for the U.S.A. According to our estimations, the underestimation using the domestic technology assumption is approximately 5-12% at the aggregate level, taking into account direct emissions. Table 2. Key relative figures of UCLM Carbon Footprint.

UCLM-Exp per student

2006

2007

2008

2009

2010

2011

2012

2013

-

-

1.10

1.26

1.06

1.15

0.92

0.72

0.74

-

-

17.48

15.87

15.88

16.58

17.64

16.36

15.95

-

-

2.85

3.50

3.17

3.40

2.70

2.06

2.13

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CF-Exp per employee

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2005

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CF-Exp per student

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-

-

26.91

30.38

29.31

30.30

28.69

21.89

20.76

CLM emissions per employee CF-Exp +CF-Wag/ CLM 2 total emissions CF-Exp + CF-Wag/ CLM residential and services 2 emissions Scope1+Scope2/ CF-Exp 2 direct emissions 2 Scope1+Scope2/ CF-Exp

34.79

33.90

34.16

30.25

29.74

29.43

28.93

29.11

-

0.33

0.34

0.32

0.36

0.38

0.43

0.40

0.36

-

5.12

5.71

5.61

5.47

5.65

6.91

6.54

5.82

-

52.07

52.64

54.51

53.94

59.67

51.69

53.94

64.97

65.36

9.33

9.69

11.17

11.82

16.33

10.83

12.38

18.72

17.85

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UCLM-Exp per employee

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Expenditure ecoefficiency 0.41 0.40 0.39 0.36 0.33 0.34 0.34 0.35 0.35 3 (CF per euro -Exp) Wages ecoefficiency (CF 0.78 0.72 0.69 0.60 0.57 0.59 0.55 0.55 0.55 3 per euro -Wag) Note: CLM stands for Castilla-La Mancha region, UCLM is University of Castilla-La Mancha, CF-Exp is the carbon footprint of UCLM taking into account only the university expenditure, and CF-Wag is the CF linked to the consumption of wages paid by the university, Exp stands for university expenditures and W for university wages. The regional emissions data comes from the National Inventories (Ministry of 1 Agriculture Food and Environment (Spain), 2015); - means non-available data. Units are in tonnes of 2 3 CO2e; are %; are kt of CO2e per euro spending. Source: Own calculations.

Although we used a different method, our results are also comparable, in broader lines, with (Ozawa-Meida et al., 2013) for Montfort University. The global CF calculation is 51 CO2 Kt, for the academic course 2008/2009 because their hybrid method calculates a measurement that considers the main, but not all, direct and indirect emissions. This measurement is higher than that of the UCLM, but it includes students commuting that are outside of the UCLM CF measures. Travels by students may explain also the higher level of the Montfort University CF per student, 2.37 CO2 tonnes per student in comparison with the lower level of the UCLM. On the whole, the results indicate that the energy-saving plan performed at the university has not resulted in environmental efficiency improvements in terms of CO2 eq. reduction. The emissions patterns have followed, in general terms, the budget pattern. Although it would 11

ACCEPTED MANUSCRIPT need a more specific analysis, it is likely that along with the recovery which could be expected when the budget constrains are overcome, the emissions will increase accordingly if measures of decoupling are not taken (Schandl et al., 2015). A further analysis along this line could be performed by applying a structural decomposition analysis (Tobarra, 2016)

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The next step is to identify the regional areas and the sectors that are the major actors in the emissions patterns. The scope 1 emissions were excluded from this discussion because those direct emissions were generated during the daily operation of the university when heating or using university vehicles, while scope 2 was present. The national pattern of the origin of emissions depicted in Figure 2 shows a relevance of imports that cannot be neglected. Imported emissions are higher in the emissions from expenditures than from wages, signifying that regional families have a slightly more closed consumption pattern than the university or that intermediate inputs more easily penetrate the regional economy than final goods. Moreover, the emissions reduction is only based on Spanish reduction (approximately 30% in both cases over the period), which is the main embodied CO2 provider, because the emissions from imports increased over the period, manly from China and America. This results highlights the need for an analysis that, on the one hand, includes not only domestic but also imported emissions and, as a consequence, is based on the consumer responsibility or footprint accounting while on the other hand providing a framework that avoids the assumption of domestic technology as the multiregional technology. Moreover, according to our estimations, the share of imported emissions in the direct emissions is much lower (approximately 10-20% and 20-30%, in emissions from expenditure and wages, respectively); thus, it is in the indirect impacts where imports increase their importance.

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Figure 2. UCLM Extended Carbon Footprint by country of origin of emissions.

Source: Own calculations.

Regarding the sectors involved in the UCLM CF, we can examine their role in two ways: upstream and downstream, depicted on the right and left side, respectively, of the cyrcos-type 12

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graphic (Krzywinski et al., 2009) shown in Figure 3 for 2009. The upstream sectors show the contribution of each sector inputs in attending the global budget to the UCLM CF. Accordingly, Figure 3 shows that energy-related sectors are key in the UCLM CF (approximately 60% of UCLM CF), with electricity as the main polluting sector (the sector is responsible for near 24% of emissions on average for the period), followed by mining and quarrying and coke, refined petroleum and nuclear fuel. Additionally, this explains the appearance as relevant contributors of sectors such as agriculture, chemicals, basic metals and rubber, a result that we have not found in previous literature; all of them are basic goods producers that generate emissions that spread along the whole economic system.

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Figure 3. Key sectors of UCLM CF for expenditure, upstream and downstream (CO2 Kt).

Source: Own calculations.

Figure 3 shows the other sectors driving those emissions by demanding the “dirty” products that the upstream sectors provide. The focus turns now to the downstream emissions (left side) on the consumer responsibility for sectors that provide inputs to the University, directly and indirectly. The more polluting inputs used by a sector, the more downstream emissions or consumer responsibility it shows. Hence, an economy, business or institution that aims to control emissions must pay special attention to the consumer responsibility measure by sectors because the sectors providing embodied emissions through intermediate inputs might 14

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be “hidden” in a distant step of the production process (far into the scope 3) and not be so evident. It is evident that the responsibility of the electricity sector is from this perspective even higher as it accounts for all of the emissions involved in the production processes of the inputs it requires, directly and indirectly, and that electricity is the main driver of emissions from mining, coke and electricity itself. However, what it may not be so evident is that more than 70%, on average, of the total emissions embodied in electricity are enabled by other sectors such renting, inland transport, electrical equipment and manufacturing; together, they surpass the responsibility of the electricity sector. The electricity sector is followed by renting, electrical equipment, manufacturing, paper and coke, and refined petroleum. All of them refer to public procurement; thus, a strategy that follows Green Public Procurement (Commission, 2011) in these areas would imply a CF reduction. Finally, sectors related to the travels, hotels and restaurants, inland transport and air transport of university employees account for almost 15% of UCLM CF. Strategies directed at increasing the awareness of the overall university community, with appropriate incentives to substitute dirtier mode of transport for more environmentally friendly modes, can have significant results.

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The evolution of the responsibility for upstream emissions in the period is quite close to that of the budget. Energy appears to be the single most contributing factor to UCLM expenditure emissions, with over 25% of total emissions and electricity as its main and increasing component (over 32% of total UCLM emission in 2013 compared to a 19% in 2005) and coke and refined petroleum with a more stable 3% share. The university also requires the provisions of services, which account for a quite stable percentage just below 20% of its expenditure emissions, and different type of manufacturing, which suffer a sharp reduction in emissions share form 13% to 4% at the end of the period. As another great contributor, transport emissions represent as a bulk approximately 10% of the total expenditure emissions and they have been reluctant to decrease their shares. Just the opposite of hotels and restaurants that reduce their share almost in a half, probably influenced by the limit set on the menu cost per person funded by the university. The conclusion indicates again the importance of energy as a fix cost hard to slow down even in a budgetary cuts scenario. However, it provides a new insight because it is not only the importance of direct energy consumed at the university but also the high energy requirement of university providers that should be addressed to reduce UCLM emissions.

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The construction sector deserves a separate mention. As mentioned before, the CF we estimated accounts for the emissions embodied in the construction of buildings used by the UCLM, but we use their depreciation values for the life time expectancy. As a result, we can observe in Figure 4 a profile of the construction sector contribution to UCLM CF smoother than we would obtain otherwise. Accounting for each year for the corresponding total investments would result in a maximum contribution of construction higher than 10% in 2009 and 2010, both resulting in overestimation of the sector contribution. It declined to nearly zero in 2012 and 2013, for instance, depending on the actual construction investments, when obviously the buildings had not disappeared and were still being used for university purposes. Larsen et al. (2013) also follow this procedure; however, they found a much higher contribution of construction to the CF of NTNU of 19%.

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Source: Own calculations.

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Figure 4. UCLM carbon footprint for university expenditure, downstream.

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This important emissions level due to procurement leads to a difficult situation because the university does not have direct control over those types of emissions. At the same time, it offers interesting opportunities for reducing emissions. A clear identification of polluting goods and services is essential for the university to select with full information its purchases. The burden of an eco-tax, which can identify more polluting goods as more expensive ones and discourage its consumption, or the use of eco-labels, which provide information on emissions embodied in goods, are two possible options. Among the decisions on efficiency that can be directly controlled by the university are those related to facilities improvement, which are expected to affect emissions in a great manner because they imply a reduction of the purchases of high-CO2-embodied goods together with a reduction of direct emissions. As a first approach to assess the impact of potential strategies, an alternative scenario is outlined for comparison. This counterfactual scenario was developed following the proposal suggested by the university in an internal proposal “Measures for Energy Savings by the university” (UCLM (University of Castilla-La Mancha), 2011). The bulk of the measures focus on the implementation of combustion-efficient systems, better use of residual heat or the acquisition of more efficient heat pumps for electrical heating. The implementation of these measures will result in an estimated reduction in gas and oil consumption of 15% and up to 40% reduction in electric consumption. Figure 5 shows the results of this savings-scenario. The average reduction in the university expenditure carbon 16

ACCEPTED MANUSCRIPT footprint is approximately 17%, with higher reductions in the last years of the period. This is a very interesting result because this improvement in the UCLM carbon footprint can be achieved with low cost based on investment, and some of the improvements can be developed while normal maintenance tasks are developed.

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Figure 5. Savings scenario.

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3.1. UCLM workers’ footprint

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The picture drawn by Figure 4 strongly depends on the expenditure pattern of the university (and on the polluting those sectors are), and consequently, it is different from the general picture shown in Figure 6, where the sectors collected are those that contribute to the UCLM ECF through wages and depend on the consumption pattern of the employees. This explains the relevance of the emissions from food, with approximately 30% of the total wage emissions as a mean for the whole period, textiles or hotels (7.43 and 4.12%, respectively). However, energy-related emissions again top the list, with 16.07% for oil-related products (where fuel for transport and heating are included) and 15.71% for electricity.

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Figure 6. UCLM carbon footprint for university wages and salaries, downstream.

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These results highlight again a situation where the main polluting sectors are not necessarily the ultimate goods and services providers for the consumers. However, although the most efficient measures must be taken at a national or even supranational level and should be focused on improving environmental practices in agriculture, energy and other sectors, there is still room for improvement at the consumer level. Potential carbon footprint abatement measures can consider the household freedom to choose as a starting point. Some environmentally respectful behaviours could be encouraged by means of the complementary salary allowances. These could be linked to sustainable consumption, such as the purchase of zero- or low-emissions vehicles, the installation of self-generated electrical energy or house isolation improvement. Although these measures can be controlled because they require the university to be informed of the household investment, some other difficult-to-monitor measures can also be implemented. Change of behaviour to a pro-environmental one is key, and, as mentioned previously, this is a matter of personal choice. In spite of that, the university could nurture consumption choices to be low-carbon-emissions ones by nudging its workers in directions that raise their environmental concerns and awareness. Increasing the number of parking spaces for bicycles, installing charging facilities for electric vehicles, easing train and public transport by signing an agreement with the service providers, promoting digital reading, and encouraging vegetarian food are some of the affordable measures that could have massive effects if environmental behaviours spread.

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ACCEPTED MANUSCRIPT 4. Conclusions

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A hybrid EEIOA in a multiregional context has been undertaken to obtain the CF for the UCLM to assess its environmental responsibility. EEIOA modelling proves very useful in gathering the complex, and often non-material, contributions of a university to a CF measure. The hybrid model addresses some problems of EEIOA, in our case by including some regional data that avoids the use of national averages and by making first-step calculations with a higher sectorial disaggregation, restricting the high heterogeneity of sectors in EEIOA. The multiregional model increases the accuracy of the results by avoiding the assumption that domestic technology or technology from another country is applied to imports, as previous research did.

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In the case of UCLM CF, indirect emissions amount to up to 80%, highlighting the need of including them in the calculations. For this reason, on-site or other ways of truncated calculation methods lead to the underestimation of emissions. The results show a UCLM CF that ranges between 23-36 kt CO2e over the analysed period with similar environmental performance per euro spent than other universities of equivalent size. These CF results show a high dependency on the budget level and evolution that would indicate a need for increasing the hybridization of the model.

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The CF analysis accomplished allows targets for mitigation strategies to be identified. The results highlight the relevance of energy-related emissions in the university context in two ways, looking at energy as the basic input widely required directly and indirectly for the university operational expenditure and also as a sector that enables emissions in others required for production. Energy related emissions demonstrate a reluctance to reduce their shares even in a budgetary cuts scenario. Affordable conservation actions in this respect could lead to a 17% reduction in expenditures emissions. However, at the same time it highlights other sectors that can be responsible for more than 70% of the total emissions embodied in electricity for instance, which is an open door to green public procurement strategies.

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Computing also the CF while considering worker choices when expending wages and salaries can help to raise awareness throughout the university community, and it would be complemented also with a CF of the students. Moreover, it allows the integration between related systems of private and working life which can be a method to achieving a shared responsibility for the environmental impacts among all of the participating and benefiting actors. To become leaders in sustainable development and to prepare students in the challenges related to it, universities should “practice what they preach”. Performing footprint analysis, such as the one conducted in the present paper in terms of carbon emissions, could perfectly fit into this strategy and provide reports, monitoring and identification of key components of environmental performance and awareness among staff and students about their role and possibilities in mitigation actions. Moreover, a similar procedure of footprint calculations can be developed for obtaining some other recommendable indicators of environmental sustainability for universities, such as total use of energy, water, materials or waste.

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ACCEPTED MANUSCRIPT 5. References

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