Ecological Footprint benchmarking of 40 Tertiary Education ...

Ecological Footprint benchmarking of 40 Tertiary Education Campuses Hilary Bekmann The Footprint Company™ Sara Rickards Macquarie University, Office of Property and Department of Human Geography Caroline Noller The Footprint Company™

Recommended Citation Bekmann, H., Rickards, S., & Nollar, C. (2013). Ecological Footprint benchmarking of 40 Tertiary Education Campuses. Proceedings of the 13th International Australasian Campuses Towards Sustainability (ACTS) Conference, Sydney, Australia.

Available at http://www.acts.asn.au/index.php/2013-conf/conference-proceedings/

Proceedings of the 13th International Australasian Campuses Towards Sustainability (ACTS) Conference 2013

Available at: http://www.acts.asn.au/index.php/2013-conf/conference-proceedings/ 2

Ecological Footprint benchmarking of 40 Tertiary Education Campuses

Hilary Bekmann1 The Footprint Company™

Sara Rickards

Macquarie University, Office of Property and Department of Human Geography

Caroline Noller The Footprint Company™

This paper discusses the use of Ecological Footprint (EF) in the reporting for both strategic planning and reporting and monitoring at university campuses. In addition, the paper presents preliminary EF results for 40 campuses calculated using an innovative software application. The EF is an evidenced based approach which quantifies human consumption, compares this to bio-productive supply and communicates this in terms of the hectares required to support that level of consumption. This approach provides an understanding of how much supply is available, how much is used and who uses what. The key strength of EF applied to campus facilities is its ability to address the life cycle of activities in and communicate the outcomes affectively. That is, “how many planets are required to support a campus?” By addressing the impact of built-form, stationary energy, water, operational consumption, transport and infrastructure in a common metric, EF provides an understanding of the comparative magnitude of these aspects and the overall impact of activities. This can be used to inform focus points for strategy, as well as support cost benefit comparisons of initiatives and outcomes. This engages stakeholders in the implications of their activities upon global sustainability. The results of the preliminary research indicate that the current average performance for Universities (2.5 Planets) demonstrates a substantial ecological sustainability challenge for campuses managers.

Keywords: Global Benchmark, Ecological Footprint, Life-Cycle Analysis, Campus, Operations

Introduction and Background

With limits to natural resources availability there is a need for practical approaches to measuring how much is consumed by people, products and services, and also determining what level of consumption is sustainable at a variety of scales. Similarly, there is a need for techniques that allow for comparative measurement and planning for ecological sustainability activities across the management portfolio of higher education campuses. This paper argues that ecological footprint analysis (EF) provides a framework that supports the monitoring, measurement and reporting of consumption at tertiary education campuses, as well as providing a meaningful target for environmental sustainability performance. This paper also presents a practical, repeatable model for assessing the EF of tertiary education campuses, which includes day-to-day consumption and embodied form, as well as allowing for future planning across universities’ management portfolio. In addition, the paper briefly reviews the preliminary results of the application of the methodology to 40 tertiary education campuses, in the United States of America (USA) and Australia. Finally the paper demonstrates the opportunities for use of the planet concept and methodology in planning through demonstration of results from an ecological footprint review of Macquarie University’s master planning options towards meeting a “one planet” target by 2030.

The model and processes to support the application presented in this paper, were developed by Macquarie University, Sydney and The Footprint CompanyTM Pty Ltd (TFC). as a response to Macquarie University’s need for a practical approach to target setting and success verification for sustainability performance in the University’s operations and facilities. The methodology logically extended to embrace operational 1 Hilary Bekmann, Vice President, Business Development, The Footprint CompanyTM, CA, USA. Ph +1 415 812 4012 Sara Rickards, Eco-Footprint Analyst, Macquarie University, NSW, AUSTRALIA. Ph +61421844595 Caroline Noller, CEO, The The Footprint CompanyTM , NSW, AUSTRALIA. Ph +61 2 8020 0786



consumption elements to provide a holistic picture of the campus ecological impact in “planet footprints”. The application is a software calculator that allows for both measurement of existing performance and scenario modelling.

There are many techniques and methodologies for capturing and reporting against environmental indicators (notably, Environmentally Adjusted Net Domestic Product (Steer & Lutz, 1993), Natural Resource Accounting (Repetto & Magrath, 1988), the Human Development Index (Sudhir & Amartya, 1994), Genuine Progress Indicator (Lawn, 2003) and the Global Reporting Initiative’s Sustainability Reporting Guidelines (GRI, 2013)). However, while these methodologies provide a framework of what to report and what areas should be targeted for consumption or emissions reduction (or bio-capacity increase), they provide no guidance on what sustainable practice would require (Dahl, 2012). Similarly, while these indicators require measurement of certain metrics, they do not provide any methodology to assess the relative value of consumption in varying areas (Shriberg, 2002). Clearly less consumption is best, but how much less must be targeted to achieve sustainable practice; and when economic resources are scarce, where should money be spent to achieve the biggest environmental gain?

In addition, (Innes & Booher, 2000) note that many detailed multi-indicator studies are enormously expensive, due to the range and complexity of data that needs to be gathered. As such, these detailed studies are rarely repeated and, without trend information have little practical use. Additionally, such one off reports are quickly out of date.

… compendia of [sustainability] indicators are seldom influential. Groups sometimes produce reports with dozens or even hundreds of indicators and then are stumped about what to do next. They want the indicators to be influential, but are without strategy for this (Pissourios, 2013)

Within the higher education sector, reporting against performance metrics is a key task of sustainability and operational staff (Huyuan & Yang, 2012 and Pineno, 2011). However, while higher education institutions who have captured data for sufficient time, typically report against sustainability metrics, covering environmental, social and (occasionally) economic metrics, there is little consistency in the approach taken by different intuitions to reporting and target setting. These indicators are typically reported either within dedicated sustainability annual report or within University general annual reports (Pineno, 2011).

To address this and to create a common measurement of sustainable performance across the sector higher education rating tools have been developed by sector sustainability associations in recent years. In the USA, the Stars rating system, administered by the American Association for Sustainability in Higher Education (AASHE) uses a point system to grade higher education institutions progress on their sustainability journey (AASHE, 2013). Similarly, the Australian and European sectors have developed the LiFE (Learning for Future Environments) index (EAUC, 2013). Both of these programs require reporting against environmental indicators (among other indicators). However, ecological reporting for STARS and LiFE reflects operational (day to day utilities, transport and waste) consumption (AASHE, 2013 and EAUC, 2013). As such, this reporting excludes embodied and life cycle costs associated with: the land used to support campus operations; the environmental cost of the material built form (and the costs of construction) and; life cycle costs of operational consumption (computers, food, paper, books etc.). Additionally, while these rating systems provide a mechanism for comparison between like institutions for greenhouse gas emissions and water use, they do not provide guidance on what an ecologically sustainable target would be.

Another key challenge for sustainability practitioners is the need understand how change to services and operations over time will impact the ecological outcomes, and how planning can strategically target improvements to campus ecological outcomes across the life cycle. A number of planning tools for community or neighbourhood developments are available through Green Building Councils (from various geographic areas). In Australia, the prevailing system, Green Star, (administered by the Green Building Council Australia, GBCA) offers a “community” tool (currently in pilot phase GBCA, 2013). Similarly, in the US, LEED (administered by the United States Green Building Council) offers a “neighbourhood Development” tool (LEED ND, 2013). Similar to STARS and LiFE, these systems award points against documented procedural measures as well as demonstrated operational outcomes. These tools provide an excellent opportunity for users to select and target areas that reflect their organisational or community strategic focus, but do not allow users to undertake comparative, quantitative assessments of the ecological benefit of measures and targets undertaken to meet tool criteria (Sinou & Kyvelou, 2006).



Ecological Footprinting and Universities

Based on the biophysical limits of supply, and presenting impacts in an aggregated, quantifiable, yet easily comprehensible form, EF provides both an indicator of current consumption and an indication of the gap between current and sustainable practice (Wackernagel, 1994).

The application of this concept to organisational analysis in general and University operations in particular is not new. Application by organisations has been explored by Chambers, Simmons and Wackernagel (2000) and Barrett and Scott (2001) among others. The methodology has been applied to University Campuses in research studies in China (Li, 2008) (Gu, 2005), the US (Venetoulis, 2001) (Conway, 2008) and Australia (Flint, 2001). It has also influenced the strategic approach of universities to campus management and planning at a number if universities, such as Pontificia Universidad Católica del Perú (Reiser, 2009), Thomson Rivers University, British Columbia (Thomson River, 2009), and Macquarie University, Sydney (Bekmann, 2012). However, with the exception of Macquarie University, which has developed it strategic approach using the method described in this paper, the methodologies used in these studies have been limited to operational expenditure, that is day to day consumption (and disposal) of utilities, goods and services, and excluded the capital or embodied cost of the land, and built form that supports the university’s operations. This limitation is largely due to difficultly in determining the costs of embodied form (Conway, Dalton, Loo, & Benakoun 2008).

Ecological Footprinting

EF analysis presents the life cycle impact of consumption in terms of bioproductive land required to support the production of the goods as well as the absorption of wastes. The calculated impact is reported in terms of global hectares per annum per functional unit (Wackernagel, 1994). Globally, it is estimated that there are 13.3 billion hectares of available bioproductive land and water on the planet. When divided by the population of the planet it results in a theoretical biocapacity / or bio productive supply per person, per annum. If the total consumption is equivalent to supply then humanity is in balance with the earth. When there is imbalance it reflects either surplus or overshoot (Wackernagel, et al., 1999).

There are a number of key benefits of EF as a metric to support sustainability reporting for higher education campuses compared to alternatives. Firstly, EF highlights how close campus operations are to ecologically sustainable. Secondly, as EF is presented on a single aggregated scale it allows for the direct comparison of the impact of different components against one another. Finally, this same aggregated figure of ecological impact, allows for comparison of environmental factors such as student/staff numbers, net income or operational budgets etc. (Barrett, 2001).

Methodology for University Campuses Assessments

The methodology section has been presented in three parts: the first section covers the ecological footprint assessment (Ecological Footprint Methodology), and; the second the data collection for the preliminary benchmarking exercise and finally (Campus selection and Data Collection).

Ecological Footprint methodology

The methodology used in this study was initially developed in Australia by TFC as a project to support ecological goals of Macquarie University, Sydney. It built upon the techniques developed to address the full LCC of real estate (expressed in ecological footprint terms) and added layers of assessment to consumptive elements, reflecting University norms. The method has been tested and refined in its application to Macquarie University Campus both for an initial footprint analysis, but also in determination of targets and strategies for footprint reduction in Macquarie University’s Master Planning.

The assessment uses a Hybrid Life Cycle Assessment (HLCA). The HLCA analysis method combines standard life cycle analysis techniques and Input-Output life cycle assessment for completeness.



The HLCA used in this study, uses actual life cycle costs quantities and where such quantities cannot be identified, the monetary value of an item is used with a gross national carbon intensity of economic end-use substituted (York 2008).

This methodology considers all impacts upstream of the point of consumption to the point of extraction (e.g. mining of iron ore for steel and coal for electricity) as well as incorporating all service and consumption inputs through monetary consideration.

The HCLA method has been selected as it reduces the limitations associated with both the standard LCA methodology, and typical Input-Output models. LCA methodology is typically imperfect due to incomplete or unreliable data courses (Reap, Roman, Duncan & Bras, 2008). In addition, input-output models in isolation don’t have the ability to demonstrate the benefit of more sustainable (which can also mean more expensive) materials (Majeau-Bettez, Strinnan & Hertwich 2011).

The principle benefit of this HLCA method lies in its completeness giving the broadest appreciation of the impact of the whole development to the point of completion (or extended operational life cycle). The key limitation of this technique derives from the use of the input-output model, that is, where the monetary value of an item is used with a gross national carbon intensity of economic end-use, environmental benefit of more responsible (and sometimes more expensive) consumption can be lost.

System Boundary

The scope of assessment includes the bio-capacity of the land used by campus operations, carbon emissions from energy use, water use (potable and non-potable), built form, infrastructure, consumption and commuting transport.

The HLCA system boundary covers all environmental costs upstream from consumption as well a year of operational activity. However, it excludes downstream impacts from waste processing and recycling. As demonstrated by Figure 1, exclusion of waste from assessment prevents double accounting, as use of post-consumer, recycled or reused materials in campus operations is considered an “upstream” cost/benefit.

Figure 1: The Footprint Company™ calculator’s system boundary Ecological Assessment System Boundary



The assessment assumes an average life cycle of 25 years for buildings and infrastructure. As such, planet ratings derived from assessments represent a 1/25 of embodied costs of buildings and infrastructure and a year of operational costs.

Campus selection and data collation

40 campuses were selected for the project; 9 from Australia and 31 from the US. The selection of campuses to study was based largely on availability of publicly available information to complete the study. That is, it required public disclosure of:

• Building Gross Floor Area managed • Carbon Dioxide equivalent emissions (scope one and two): (or sufficient information for this to be

derived) • Water usage • Consumption information from University annual financial reports and • Transport mode norms for University staff and students

The requirement for organisations to have this data publically available to be included in the study was self-limiting, as it required selection of campuses far enough along the sustainability journey to collect and publically disclose this data. It also excluded a number of organisations with discrete sustainability reporting, but consolidated financial reports, such as a number of State University systems in the US, as well as State administered Further Education institutions in Australia, who report sustainability data at institute level, but report financially at State level.

For Australian Universities, data was derived from university websites, university sustainability annual reports, and annual financial reports. However, in the case of Macquarie University, Sydney a detailed assessment of university utilities databases, building infrastructure and financial records was undertaken. Due to timing of the study and the reporting periods for Australian University’s, the performance year for Australian Universities was 2011.

In the US, data was derived similarly; however, much of the environmental data was already aggregated by public declarations on the AASHE stars system website. That is: building area, carbon emissions, water use and modal share. However, as STARS is a points system, where reporters target points that are achievable, not all reporting organisations report in all areas required by the EF assessment. Organisations who declared all four were, typically, those who performed well in the Stars rating system. This confirmed that availability of data tends to reflect advanced sustainable performance. Of the 30 US organisations studied, sustainability related data was sourced from the STARS website for 20 organisations, for the other 10, it was found from various sources. For these 10, water was most challenging to source accurately, and was often derived from statements such as “XXXX campus uses approximately X Million Gallons of water per year”. Some Carbon declarations in this group were also a number of years old (the oldest being 2009), and unless savings were otherwise stated, previously recorded growth trajectory of carbon emissions was extended in a straight line to the performance year. Due to differing reporting cycles (and report release dates) US University’s performance years were either 2011 or 2012.

Financial data was the easiest data to locate, but most difficult to standardize. Financial data used within assessments includes:

• Funds spent on renewal and regeneration of stock (refurbishments, maintenance of plant and equipment etc.)

• Funds spent on consumption – where possible broken down into sector specific spend (appliances, food and beverages, computers, books etc.)

Typically, these were found in the Profit and Loss sections of annual reports and were provided with varying degrees of detail. Many organisation’s in the US listed expenses by school/discipline rather than activity type, limiting the usefulness of their financial data for the assessments. These organisations were excluded from the assessments due to a lack accurate data.



Where funds spent on renewal and regeneration of stock (refurbishments, maintenance of plant and equipment etc.) was not available, annual depreciation for buildings and infrastructure was used as a proxy. This substitution was based on an assumption that funds spent on maintenance and renewal approximately equals annual depreciation.

Biocapacity was determined using either annual sustainability reports or from a review of Campus maps and satellite photos and available information. The key metrics required for the assessment include built on area, external impervious surfaces and an identification of areas of high value either as sources of food supply or biological diversity.

The embodied form of buildings was assessed using estimates based on building type. From publically available information it was impossible to determine floor areas of different building types in each campus. As such, estimations were made based on a combination of sources. Building uses (library, admin, teaching and learning etc.) were determined from campus maps, typical construction type and number of floors (i.e. 3 story brick, 6 story block and curtain wall) was determined by either a visit to campus, or Google Earth/Google Street view walk through of campus sites. The percentage of each building type and use was then estimated. From percentage estimates, total m2 of each use and type were determined as a proportion of total disclosed floor area. Where residential numbers and or lab space was publically disclosed (typically as part of STARS reporting), this data was cross referenced with building type. Input into the calculator for these buildings is a dropdown list of building types, for example: 3 story brick admin, 4-5 curtain wall lab etc. with the usable floor area of each type entered into the system. Each of these building types has an estimated Global meters squared (Gm2) of impact for every m2 of building. These estimates have been determined using construction cost figures for these building types within Australia (Rawlinsons, 2011) and checked against actual LCA assessments of 65 buildings at Macquarie University. While there was information available on transport mode share of students and staff average distance travelled was not available. As such, average distances recorded by Macquarie University, Sydney were used as a proxy for all Campuses. Findings The findings section is presented in two parts. Firstly the findings of the preliminary benchmarking study are presented, followed by some high level results from Macquarie University’s master planning exercise. A table of classification institutions and ecological footprint aspects is provided as Table 1. Given limitations in data consistency (that is an inability to determine that datasets captured and reported cover the same operational boundaries), findings are indicative only of the general size of footprint aspects and some trends requiring further data sets to confirm.

Table 1: Statistical data across all campuses (Australia and USA) included in the initial study

Planets Biocapacity Energy Water Buildings Infrastructure and repairs

Operational Expenditure Transport

Average (GHa)

2.55 Planets 1,174 27,109 457 40,148 21,060 57,004 2,237

Average % of total Footprint

NA 1% 19% 0% 28% 15% 39% 2%

Median 1.99 270 20,051 192 23,375 8,601 31,107 1,959 Maximum 7.00 16,870 140,763 5,197 180,241 110,132 311,442 8,037 Minimum 0.40 56 404 1 677 145 54 80 Range 6.60 16,814 140,359 5,196 179,564 109,987 311,388 7,958



Figure 2: Average performance data across campuses included in the initial study Australian results

Table 2: Statistical data across Australian campuses included in the initial study



AVERAGE (GHa)

1.32 Planets 254.06 21339.04 69.89 8207.8 9018.39 26145.88 2737.76

AVERAGE % of total Footprint

NA 0% 31% 0% 12% 13% 39% 4%

Median 1.3 186.1 13961.0 55.1 7917.0 6571.8 20610.4 1826.7 Maximum 2.0 705.2 77367.9 120.6 14442.9 20820.7 60891.1 8037.1 Minimum 0.8 86.4 5122.5 28.3 2238.1 2143.1 6696.5 1097.1 Range 0.7 519.1 63406.9 65.5 6525.9 14248.9 40280.7 6210.4

Figure 3: Average performance data across Australian campuses included in the initial study



Figure 4: Performance data across Australian campuses included in the initial United States results

Table 3: Statistical data across United States campuses included in the initial study



AVERAGE (GHa)

2.8 Planets 1389.1 27861.0 549.0 47642.2 26446.9 58209.8 2032.6

AVERAGE % of total FP

NA 1% 17% 0% 29% 16% 35% 1%

Median 2.7 298.6 21818.0 288.9 33343.0 11619.3 27279.2 2090.5 Maximum 7.0 16870.1 140762.5 5197.0 180241.4 110132.0 311441.8 6236.2 Minimum 0.4 56.3 404.0 0.7 677.4 145.3 0.0 79.5 Range 6.6 16813.8 140358.5 5196.3 179564.0 109986.7 311441.8 6156.7

Figure 5: Average performance data across United States campuses included in the initial study



Figure 6: Performance data Unites States campuses included in the initial study (1 of 3)

Figure 7: Performance data Unites States campuses included in the initial study (2 of 3)



Figure 8: Performance data Unites States campuses included in the initial study (3 of 3) As noted above, average ecological footprint across all campuses analysed was 2.5 planets. The average of Australian University’s was significantly lower than that of US universities (Australian Universities 1.3, US universities, 2.8). The three areas of greatest ecological impact for campus managers are: operational expenditure average of 39% impact, Buildings average 28% and Energy use 19% impact. Using this methodology, water, transport, and bio-capacity appear to have far less impact in overall terms. It is worth noting however, that the cost of transport identified in the table above is operational only, and impact of vehicles on infrastructure and repairs sections of assessments is likely high and warrants further investigation. Surprisingly, residential intensity of University’s surveyed made no noticeable difference to planet results, with averages for primarily non-residential, highly residential and primarily residential, all falling in the range of 3 +/- 0.2 Planets. However, a lower planet rating and global meters squared (Gm2) per Equivalent Full Time Person EFT (staff and student) for Australian Universities may reflect a tendency for Australian Universities to have lower centrally managed student housing, or exclude student housing from reporting boundaries. Australian Universities average 20,000 Gm2 per EFT / per annum (pa), whereas US universities average 55,000 Gm2 per EFT/pa. Discussion The challenges in data availability (and boundary matching) in this initial study limit the ability to determine absolute outcomes for campus footprints. However, the preliminary benchmarking has highlighted: an indication of size of the sustainability “gap” between current and ecologically sustainable practice, key areas of focus for Campus Managers and areas for further analysis/study Based on an analysis of 40 campuses, average performance of University Campuses is 2.55 planets.



However, some Campuses are operating at significantly higher ecological efficiency levels than others. The footprint of the average Australian is 85.5% of that the average American (WWF, 2008), yet Australian campuses surveyed in this study rated 46% of the US average. The significantly lower average of Australian Universities compared to US universities (even compared to national norms) is likely due to both limitations in the sample size and also the different types of service offered by US universities, which often host professional sporting teams, stadiums, as well as higher percentage of residential students than is the norm within Australian institutions. This can be seen in the comparative space allocation per EFT in each country in the universities surveyed. As demonstrated by table 4 below, space allocation per student in Australian Universities included in the study is less than 30% of that in the USA Universities in the study, and space allocation per full time person (EFT) less than 35% of the US comparison.

Table 4: Gross floor area per student and per equivalent full time person comparison Australian and

US Universities surveyed

Surveyed Universities Gross Floor Area /student (m2)

Gross Floor Area / Equivalent Full Time Person (m2)

United States of America 51.52 37.10 Australia 14.06 12.48

As campuses grow, and the role of universities change, the question is how to reduce the ecological footprint of campuses to sustainable levels, particularly for those campuses selling a premium “brand” to staff, students and the broader community. How do campus managers make assets go further, support more students, more research and more staff whilst maintaining the levels of service, output and reputation which defines the sector? The strategic planning undertaken for Macquarie University, Sydney provides an example how this might be approached. Strategic planning In a separate but related study, of Macquarie University Campus Masterplan, has begun to be addressed this question through using EF modelling to determine the best approach to creating an ecologically sustainable outcome for the campuses future, modelling the future scenarios for campus growth and the likely environmental impacts of future growth. Figure 9 and 10 below demonstrate the outcomes of the study, at a high level. These serve to demonstrate the opportunities for use of the methodology in strategic planning as well as in sustainability reporting, rather than necessarily providing outcomes and thus form part of the discussion section of the paper. Methodology strategic planning At Macquarie University, the methodology was applied to assist the University to meet the “One Planet” goals for its master plan. Macquarie University had a well developed detailed baseline for its 2011 campus operations at 1.34 Planets with a comprehensive understanding of the energy and water use per building as well as for the campus as a whole. Using this data, as well as campus master plan growth plans, a business as usual (BAU) growth plan (that reflect the university’s sustainable buildings approach) was modelled to reflect high and low growth trajectories.



• Gross Floor Area (GFA) growth and building type had been modelled for the campus as part of master planning exercises. This data informed the rest of the master planning modelling.

• Energy and water were modelled based on University targets for each building type, reflecting the current fuel mix at the university and extending the annual decline in NSW mains power carbon coefficient seen from 2006 – 2012 in NSW (GGAS, 2011) in a straight line from 2013 – 2030.

• Embodied form for BAU was taken as that calculated from a recent mixed use development on the campus.

• Staff and student numbers were calculated using the Tertiary Education Facilities Management Association (TEFMA) guidelines (TEFMA, 2009) for building type against GFA growth.

• New and extended building footprints were calculated and changed in the assessment where this changed 2011 land practices (increased hard surfaces, decreases to parkland etc.).

Once business as usual growth was modelled, innovation approaches were developed to determine pathways to reach the University’s one planet goals. In particular targets for new buildings, student load and greenhouse emissions. Preliminary results of the study are demonstrated in figures 9 and 10 below.

Figure 9: Macquarie University, Sydney absolute Ecological Footprint of Campus growth scenarios

Figure 10: Macquarie University, Sydney absolute EF of Campus growth scenarios



Modelling future scenarios allows for targets to be strategically set to reduce the impacts of campus activities across the life cycle. Future applications could involve testing sustainability initiatives for their life cycle impact per dollar spent. With this information, campus mangers can begin to address targets in key areas to reduce their ecological footprint as they plan for new developments, student load and set procurement policies. The key areas of focus for campus ecological sustainability, highlighted by the study, are operational spending per EFT, energy use, built form and potentially transport. These focus areas are likely no surprise for Campus managers, however the ability to confirm the relative importance of these areas and the constituent components of each, using quantitative metrics as well as measure success/failure in these areas is less established. Of particular value is the ability to measure and quantify the value of more efficient asset management (persons per square meter of space) and consumption across the life cycle. A key strength of applying EF using a custom built software tool for the higher education sector is an ability to undertake on-going reporting without a prohibitive time penalty. Once the initial parameters have been developed by an organisation (what is in and what is out of the assessment) and the data streams established, the software package can be simply used to undertake annual reporting, model future growth or development and operational changes, and also to compare the value of different strategic approaches upon the campus footprint. This allows a targeted approach to spending, to ensure that dollars spent are targeted towards the greatest possible impact. The key barrier to the use of this methodology in the past has been the complexity of the calculations, the availability of data, the non-standardization of the approach and the inability of practitioners to determine the LCA cost of embodied form. The TFC calculator, and the HCLA method employed allows for data to be progressively gathered at the material level, and dollar values to act as a proxy for quantity data when this is not known. There are a number of areas of potential further study, however the key areas for future study include:

• Studies to determine the value of EF for campus strategic planning in particular uses in asset planning and management

• The relative value of sustainability initiatives using an EF metric for quantitative comparison • The value of the EF framework as an engagement and communication tool for staff and students • Validation of the methodology for campus buildings; and a detailed review on using building

footprints for briefing Conclusion If sustainable development is to be achieved, learning institutions have a responsibility beyond that of others organisations as they have a unique ability to raise awareness and influence the behaviour of future generations (McNichol, Davis, & O’Brien 2001).

The value of EF as an analysis tool for companies, communities and for higher education campuses is well established in the literature. An understanding of the life-cycle ecological impact of activities upon the environment, and the output representations against a finite biological supply is valuable for campus managers on a number of levels. It provides a meaningful target, without prescribing an approach to achieve it. It allows for a scalable target, allowing campus managers can set targets in Global meters squared per aspect (energy, water, buildings) or meter squared, per building, per student, or for the campus as a whole. It also provides a metric that encourages and rewards practices which are known to be sustainable but are hard to capture using traditional methods, as well as the metrics to determine what scale technologies need to be applied to replay the cost of their applications. That is, adaptive re-use of buildings versus rebuilding; sizing plant and equipment to produce cleaner energy or treated water to pay back



embodied cost; re-use of computers and equipment; efficiency in timetabling; e-books versus paper; the cost or storing equipment and samples versus simulation opportunities etc. The availability of a standardised system, which allows for varying levels of data availability, but can still assist in the decision making process makes the process of employing the EF as a sustainability indicator a comparatively simple one for campus managers. References AASHE. (2013). ASSHE stars FAQ page. Retrieved July 10, 2013. from ASSHE:

https://stars.aashe.org/pages/about/faqs/ Barrett, J., & Scott, A. (2001). The Ecological Footprint: A Metric for Corporate Sustainability. Corporate Environmental Strategy, 8(4), 316–325. doi: 10.1016/S1066-7938(01)00132-4 Bekmann, H. (2012). Macquarie University ESD Strategy: One Planet 2030. Macquarie University, Sydney. Carnegie Institute (2013) The Carnegie Classification of Institutions of Higher Education™. Retrieved 7 10,

2013, from The Carnegie Classification of Institutions of Higher Education™: http://classifications.carnegiefoundation.org/

Chambers, N., Simmons, C., and Wackernagel, M. (2000) Sharing nature's interest: ecological footprints as an indicator of sustainability. London: Earthscan.

Conway, T. M., Dalton, C., Loo, J., & Benakoun, L. (2008). Developing ecological footprint scenarios on university campuses: a case study of the University of Toronto at Mississauga. International Journal of Sustainability in Higher Education, 9(1), 4-20. doi: 10.1108/1467637081084215

Crawford, R. H. (2008). Validation of a hybrid life-cycle inventory analysis method. Journal of Environmental Management, 88(3), 496-506. doi: 10.1016/j.jenvman.2007.03.024

Dahl, A. L. (2012). Achievements and gaps in indicators for sustainability. Ecological Indicators, 17, 14-19. doi: 10.1016/j.ecolind.2011.04.032

Dawe, G., Jucker, R., & Martin, S. (2005). Sustainable development in higher education: current practice and future developments. A report for the Higher Education Academy. http://thesite.eu/sustdevinHEfinalreport.pdf

DETYA. (2000). Benchmarking: a manual for Australian universities. Department of Education, Training and Youth Affairs, Higher Education Division. Canberra, Australian Capital Territory. doi: 10.1080/13504622.2011.572161

EAUC. (2013). The Life Index Home Page. Retrieved July 10, 2013, from The Life Index: http://www.thelifeindex.org.uk/

Flint, K. (2001). Institutional ecological footprint analysis - A case study of the University of Newcastle, Australia. International Journal of Sustainability in Higher Education, 2(1), 48 - 62. doi: 10.1108/1467630110380299

GBCA 2013 Green Star – Communities, Green Building Council of Australia. Retreved July 21st, 2013 from: http://www.gbca.org.au/green-star/green-star-communities/

GGAS, 2011 GGAS: Fact Sheet – The NSW Pool Coefficient. Retreved June 20th, 2013 from: http://www.greenhousegas.nsw.gov.au/documents/FS-Comp-PoolCoeff-Oct11.pdf

GRI. (2013). G4 Sustainability reporting guidelines. Retrieved July 15, 2013 from Global Reporting Initiative: https://www.globalreporting.org/Pages/default.aspx

Gu, X. W., Li, G. J., Wang, Q., Liu, J. X., Ding, Y., & Liu, J. Z. (2005). Ecological Efficiency of High Education——Ecological Footprint of University Campus. Journal of Glaciology and Geocryology, 3, 014. doi: NA

Huyuan, L. & Yang, J. (2012) Overcoming Organisational Resistance to Sustainability Innovations in Australian Universities. Proceedings of the 12th Annual Australasian Campuses Towards Sustainability (ACTS) Conference, 26-28 September 2012 .Brisbane, Australia. http://www.acts.asn.au/index.php/2012-acts-conference/proceedings/

Innes, J. E., & Booher D.E. (2000). Indicators for sustainable communities: a strategy building on complexity theory and distributed intelligence. Planning Theory Practice, 1(2), 173–186. doi:10.1080/14649350020008378

Kim, B., Houser, L. P., Rosenthal, A., & Neff, R. (2008). Literature Review of Methods and Tools for Quantifying the Indirect Environmental Impacts of Food Procurement. The Johns Hopkins Centre for a Liveable Future, Johns Hopkins Bloomberg School of Public Health, Baltimore. http://sustainability.universityofcalifornia.edu/documents/jhufoodprint_rpt.pdf

Lawn, P. (2003). A theoretical foundation to support the Index of Sustainable Economic Welfare (ISEW), Genuine Progress Indicator (GPI), and other related indexes. Ecological Economics, 44(1), 105–118. doi: 10.1016/S0921-8009(02)00258-6.



LEED ND 2013 LEED – Neighbourhood Development, United States Green Building Council. Retreved July 21st, 2013 from: http://www.usgbc.org/neighborhoods

Li, G., Wang, Q., Gu, X., Liu, J., Ding, Y., & Liang, G. (2008). Application of the componential method for ecological footprint calculation of a Chinese university campus. Ecological Indicators, 8(1), 75-78. doi: 10.1016/j.ecolind.2007.01.007

Majeau-Bettez, G., Strømman, A. H., & Hertwich, E. G. (2011). Evaluation of process-and input–output-based life cycle inventory data with regard to truncation and aggregation issues. Environmental Science & Technology, 45(23), 10170-10177. doi: 10.1016/j.jenvman.2007.03.024,

McNichol, H., Davis, J. M., & O’Brien, K. R. (2011). An ecological footprint for an early learning centre: identifying opportunities for early childhood sustainability education through interdisciplinary research. Environmental Education Research, 17(5), 689-704. doi: 10.1080/13504622.2011.57216

Pineno, C. (2011) Sustainability Reporting by Universities: A Separate Category Within the Balanced Scorecard Based on Key Drivers Through a Mapping Strategy. Journal of Strategic Innovation and Sustainability, 7(2), 122-134. doi:NA.

Pissourios, I. A. (2013). An interdisciplinary study on indicators: A comparative review of quality-of-life, macroeconomic, environmental, welfare and sustainability indicators. Ecological Indicators, 34, 420-427. doi:10.1016/j.ecolind.2013.06.008

Rawlinsons. (2011). Rawlinson’s Construction Cost Guide. Perth, WA, Australia: Rawlinson. Reap, J., Roman, F., Duncan, S. & Bras, B. 2008 A survey of unresolved problems in life cycle assessment.

The International Journal of Life Cycle Assessment 13(5), 374-388 doi: 10.1007/s11367-008-0008-x. Reiser, J. (2009). University Footprint Calculator. Departamento Académico de Arquitectura, Peru. Repetto, R., & Magrath, W. (1988). Natural resources accounting. Washington, DC: World Resources

Institute. Shriberg, M. (2002). Institutional assessment tools for sustainability in higher education: strengths,

weaknesses, and implications for practice and theory. Higher Education Policy, 15(2), 153-167. doi:10.1016/S0952-8733(02)00006-5

Sinou, M., & Kyvelou, S., 2006 Present and future of building performance assessment tools Steer, A., & Lutz, E. (1993). Measuring environmentally sustainable development. Finance and

Development, 30, 20-20. doi: NA Sudhir, A., & Amartya, S. (1994). Human development Index: Methodology and Measurement. Paper

provided by Human Development Report Office (HDRO), United Nations Development Programme (UNDP) in its series Human Development Occasional Papers (1992-2007) with number HDOCPA-1994-02.

TEFMA (2009) TEFMA: Space planning guidelines v3. Retreved June 20th, 2013 from: http://www.tefma.com/uploads/content/26-TEFMA-SPACE-PLANNING-GUIDELINES-FINAL-ED3-28-AUGUST-09.pdf

Thomson River. (2009). Ecological Footprint Analysis and Steps Forward. Retrieved 09 20, 2012, from Thomson River Sustainability pages: http://www.tru.ca/sustain.html

WWF (2008), Living Planet Report. Retreved June 5th, 2013 from: assets.panda.org/downloads/living_planet_report_2008.pdf

Wackernagel, M. (1994). Ecological footprint and appropriated carrying capacity: a tool for planning toward sustainability. Thesis, Wackernagel, M.

Wackernagel, M., Onisto, L., Bello, P., Callejas Linares, A., Susana López Falfán, I., Méndez Garcıa, J., & Guadalupe Suárez Guerrero, M. (1999). National natural capital accounting with the ecological footprint concept. Ecological economics, 29(3), 375-390. doi: 10.1016/S0921-8009(98)90063-5

York, R., Rosa, E. A., & Dietz, T. (2004). The ecological footprint intensity of national economies. Journal of Industrial Ecology, 8(4), 139-154. doi: 10.1162/1088198043630487

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