Low Carbon Diet: Integrating Gastronomy Service Emissions into the Carbon Management of the University of Graz

Abstract

To avert the upcoming crisis of climate change, significant changes on different scales and sectors are necessary. The knowledge and research of the higher education sector is an essential part in the fight against climate change already. Many universities admit the urgency of acting within their institution as well and have started to measure their impact on the environment to formulate emission-reduction goals. As part of its sustainability strategy, the University of Graz launched the Institutional Carbon Management (ICM) project to calculate its emissions via a greenhouse gas emissions inventory. In comparison to other inventories, the ICM also includes the gastronomy services on and around the campus of the University of Graz, which is also the focus of this paper. It was found that especially energy- and carbon-intensive food products such as meat and dairy contribute to the emissions of a gastronomy service. In total, the gastronomy service emissions contribute 1.1% to the total emissions inventory of the university. Although the contribution is a rather small portion, the University of Graz sees itself responsible for all its emissions and therefore also aims to gain comprehensive insights into all sub-areas of its institution to formulate validated reduction pathways. The changes to a more sustainable gastronomy and low-emission diets can therefore be seen as part of a wider change towards more environmentally friendly behaviour in general with the overall aim to meet the Paris climate goal.

1. Introduction

Climate change and the associated increase in the global average temperature is a severe problem for present and future generations as it has widespread impacts on human and natural systems. To reach the Paris Agreement’s target to limit the increase of global average temperature below 1.5 °C compared to pre-industrial levels and avert the upcoming crises, significant changes on global, national, and local scales in different sectors are necessary. The higher education sector is already an essential pillar in the fight against climate change, as its researchers contribute to the understanding of climate change and its impacts while also educating students and society. However, many universities also admit the urgency of acting more sustainably within their institutions [1,2,3,4,5].

A widely accepted concept nowadays is to measure the university’s impact on the environment to formulate specific emission-reduction goals. For that reason, many universities use greenhouse gas (GHG) emissions inventory methods. The Greenhouse Gas Protocol Standard provides a general guideline for conducting GHG emissions inventories [6]. Furthermore, Santovito and Abiko [7] provide recommendations for preparing a GHG emissions inventory, especially for university settings. They summarise that the most commonly assessed emission sources for universities’ GHG emissions inventories are heating and cooling systems, refrigerants, fertilisers, purchased electricity, transport, air travel, solid waste, and wastewater. Although these emissions sources are frequently used, the universities’ scopes demonstrate great varieties. While some universities mainly focus on electricity usage and commuting, others additionally include waste or purchased products in their calculations [8,9,10]. Nevertheless, one area that is hardly ever part of a universities’ GHG emissions inventory is the university-related gastronomy. Yañez et al. [5] mention the inclusion of cafeteria and dining commons, but only for energy usage in CO₂ equivalent per m³. In addition, Ridhosari and Rahman [11] and Güereca et al. [8] assessed organic waste or food waste as a subcategory of solid waste in their calculation. Still, food products themselves were not evaluated in these papers. Larsen et al. [12] include food services (e.g., for meetings) in their carbon footprint. However, a further specification of the services’ composition and calculation is not provided and not the paper’s focus. Mendoza-Flores et al. [13] include meat consumption in the dining hall that the university operates. Here, the most common types of meat (beef, pork, chicken, and fish) are part of the emissions inventory. In Austria, the Alliance of Sustainable Universities [14] was founded to anchor sustainability concerns in Austrian universities. It developed a GHG emissions inventory tool for calculating GHG emissions of the higher education sector [3,15,16,17]. This tool includes an optional module for the inclusion of canteens, comprising the food products beef, pork, fish, and fats and oil [15]. Other food products or gastronomy services (GS), such as cafés or food stands, were not included. In general, the contributions to a full gastronomy service’s environmental impact in literature are few [18,19].

At the same time, universities often have several thousands of students, lecturers, and employees, influencing the gastronomy at their campus and campus surroundings. It is also known that the agricultural sector, and therefore food products, are an essential contributor to global GHG emissions. In 2010, the agricultural sector was responsible for 10–12% of worldwide anthropogenic GHG emissions [20]. Moreover, agriculture has a significant influence on the planetary boundaries, especially on climate change, nitrogen cycling, phosphorus cycling, freshwater use, biodiversity loss, and land-system change [21]. In addition, dietary choice and associated food products are considerable influencing factors for the environment. The food products’ environmental impacts vary widely depending on several factors such as animal or plant origin, water and fertiliser use, or required land use. Many researchers conclude that a transition towards mainly plant-based diets result in a reduced planet burden and decreased GHG emissions per person [22,23,24,25].

As part of the Alliance of Sustainable Universities in Austria and embedded in its Sustainable University of Graz strategy, the University of Graz launched the Carbon Management project, with the focus on Institutional Carbon Management (ICM) [1,26]. In general, this project forms the basis for ambitious carbon reduction targets within the university and provides assistance towards a low carbon transition with the overall aim to meet the Paris climate goal. One main goal of the project is to calculate an ICM Reference Budget 2020 that reflects the annual emissions of the former decade. This budget is then the basis for emission reductions achieving net-zero emissions by 2030 and climate neutrality by 2040 [26].

The ICM at the University of Graz comprises the main modules (action fields) energy, mobility, resources, and stock changes to evaluate its GHG emissions. To obtain a holistic picture, the University of Graz included gastronomy services as part of third-party services in the action field resources in their GHG emissions inventory. Here, the focus lies on the food products and energy consumption of the GS. Therefore, the accounting tool of the ICM project was adapted to the specific needs of gastronomy services.

The purpose of this paper is the calculation and estimation of the gastronomy services GHG emissions related to the University of Graz as part of the calculation of the ICM Reference Budget 2020. Furthermore, this paper aims to give guidance on how gastronomy services can be calculated and analysed. In addition, it provides insights into the emission intensity of different gastronomy services and related food products.

The paper is structured as follows: methods and materials are outlined in Section 2; the results are presented in Section 3. Section 4 provides a discussion of the results and the general approach of the paper. Conclusions and implications for further research are given in Section 5.

2. Materials and Methods

2.1. The GHG Emissions Inventory

The conceptual and methodical foundation for calculating the GHG emissions inventory is the GHG Protocol Standard, which is a well-accepted tool developed by the World Resource Institute and the World Business Council for Sustainable Development [6]. Although the standard is primarily written for corporations, it is nevertheless also well suited for other institutions. It can be seen as a guideline that needs to be adapted to executing an organisation’s specific needs. Nevertheless, the principle’s relevance, completeness, consistency, transparency, and accuracy, on which the standard is based on, is valid for any organisation. The standard recommends splitting the emissions into direct and indirect emissions, as well as into scopes. Direct emissions originate from emissions sources that are owned or controlled by the organisation. In contrast, indirect emissions result from the organisations’ activities but originate outside the organisation. More specifically, the emissions are divided into three scopes. Scope 1 emissions are direct GHG emissions that are owned or controlled by the institution. Examples are the generation of electricity or processing activities on site. Scope 2 emissions are electricity indirect GHG emissions from consumed electricity. In contrast, the optional scope 3 emissions are other indirect GHG emissions that are not controlled by the institution but are a consequence of its activities and can therefore vary. Typical scope 3 emissions are transport-related activities or waste disposal.

The University of Graz includes all three scopes in its inventory. As a service institution, it follows the cradle to gate approach, including upstream activities in its balance, for example, employee commuting, purchased goods and services, or stocks and assets. The ICM project is split up in 4 main modules or action fields: energy, mobility, resources, and stock changes, further divided into 11 action subfields. Gastronomy services are part of the action field resources and add to the Scope 3 emissions of the university.

2.2. The Carbon Management Approach

In general, the carbon management approach of the University of Graz is structured along six main steps or key elements: (1) define actors and action fields, (2) estimate a reference budget, (3) adopt a reduction target path, (4) prepare tables of actions and measures, (5) set up quarterly and annual emissions monitoring, and (6) implement a dynamical decision support workflow for integrated overall guidance [1,26].

More specifically, the three main tasks of the Institutional Carbon Management of the University of Graz are to (1) provide an ICM reference budget 2020 based on annual emissions from 2015–2019, (2) develop an ICM strategy and implementation plan for 2021–2030, and (3) prepare an ICM monitoring for 2021–2030 comprising accompanying research [26]. This paper provides the results of the gastronomy GHG inventory for the University of Graz, which is included in the university’s total GHG emissions budget (energy, mobility, resources, and stocks). The total emissions budget is called the carbon management (CM) reference budget 2020. The CM reference budget represents the annual average emissions from the previous decade, 2010 to 2019. Because of difficulties in obtaining data before 2015, the data for the reference budget of the ICM project are collected for the years 2015 to 2019. Afterwards a weighted average is applied on the data, where the annual emissions of the first years 2015 and 2016 receive a higher weighting factor than the following years (2017, 2018, 2019) [26], see Equation (1). This ensures fair accounting for the emissions of the past decade:

RefBudget2020 [CO₂eq] = GHG₂₀₁₅ × 0.25 + GHG₂₀₁₆ × 0.25 + GHG₂₀₁₇ × 0.2 + GHG₂₀₁₈ × 0.15 + GHG₂₀₁₉ × 0.15

(1)

2.3. The GS Tool

An Excel-based tool for data gathering, collection, and analysis of the gastronomy services was developed. To simplify data collection, the tool comprises two main emission sources: energy and food products. Energy was split up in electric and thermal energy, collected in kilowatt-hours (kWh). Because of the high usage in restaurants, it was chosen to include meat, dairy, coffee, fats and oils, and vegetables and fruit products, collected in kilograms (kg). The calculation was performed via multiplying the activity data (AD) with the corresponding emission factor (EF), resulting in the amount of GHG emissions (GHG):

$$GH{G}_{Energy} \left[{\mathrm{CO}}_2\mathrm{eq}\right]=\mathrm{AD} \left[\mathrm{kWh}\right] \times \mathrm{EF} [{\mathrm{CO}}_2\mathrm{eq}/\mathrm{kWh}]$$

(2)

$$GH{G}_{Food} \left[{\mathrm{CO}}_2\mathrm{eq}\right]=\mathrm{AD} \left[\mathrm{kg}\right] \times \mathrm{EF} [{\mathrm{CO}}_2\mathrm{eq}/\mathrm{kg}]$$

(3)

The emission factors for energy were taken from the ICM tool, the Environment Agency Austria, and from Chiari et al. [15,26,27]. For food products, the emission factors from Reinhardt et al. were chosen [28]. Reinhardt and colleagues wrote a study about the ecological footprints of food products and dishes in Germany. Amongst others, the study was chosen because of the vast amount of analysed food products and the assumed similar conditions for Germany and Austria. In total, they analysed 188 food products via life cycle assessments according to ISO 14040 and ISO 14044, with the system boundary at the supermarket check-out. In a sensitivity analysis, Harrer [29] compared different studies with emission factors for food products. The findings confirmed that studies with similar assumptions, conditions, and methods lead to similar results, meaning that using the same or very similar studies improves the consistency of the research [29].

Table 1. Framework of the GS Tool including emission factors used.

Category	Product Group	Product	EF [kg CO2eq]	Year (s)
Electric Energy [kWh]	Electricity	Electricity Use with certificate	0.030	-
		Electricity Use without certificate	0.279	-
Thermal Energy [kWh]	Heating	Natural Gas Use Heating	0.238	-
		Heating Oil Use extra light	0.305	-
		Heating Oil Use light	0.322	-
		Coal Use Heating	0.408	-
		Solar Heating	0.023	-
		District Heating ‘Energie Steiermark’	0.322	-
		District Heating Mixture	0.186	-
Food Products [kg]	Meat	Beef	13.600	-
		Pork	4.600	-
		Chicken	5.500	-
		Fish 1	5.833	-
	Fats & Oil	Fats & Oil	3.200	-
	Dairy	Butter	9.000	-
		Cheese Mix	5.700	-
		Milk	1.400	-
	Coffee	Coffee	5.600	-
	Veg/Fruit	Veg/Fruit 2	0.425	-

¹ Mean ‘Fish, aquaculture’, ‘Fish, wild caught, bulk frozen’, ‘Fish, wild caught, speciality, frozen’. ² Mean ‘Apple, average’, ‘Avocado’, ‘Banana’, ‘Strawberries, fresh, average’, ‘Carrots, fresh’, ‘Orange’, ‘Pepper’, ‘Cucumber without plastic packaging’, ‘Salad mix washed’, ‘Tomatoes, fresh, average’, ‘Grapes, fresh, average’, ‘Zucchini’. The background colour of the table is for better readability, as the rows become clearer.

presents the general framework of the GS tool with the corresponding emission factors used.

2.4. Data Collection and Analysis

For data collection, the six gastronomy services located around the main building of the University of Graz were contacted via e-mail, including an invitation to participate plus additional information. After a positive reply, meetings in person were arranged. Specific enquiries were clarified mainly via telephone or e-mail. Whenever possible, data were collected in quarters for the years 2015 to 2019. Gaps in quarters or years were filled with mean values from the existing data. It was possible to gather data from all gastronomy services on campus, which include two food stands, two cafés, and two canteens.

The provided data of the different gastronomy services vary in their comprehensiveness and accuracy. This is mainly due to the different approaches by which purchases are documented. Three out of six gastronomy services had accurate delivery notes on food products for the specific periods, while the others could only estimate the required data. In that case, accurate data were either not available or too time-consuming to obtain. Estimations were always performed by the owner or the purchasing manager of the GS. In addition, not every gastronomy service was able to include the whole list of food products. These food products are either not used within the GS or data were not available. However, the most used food products for the considered gastronomy service are covered.

In contrast, it was possible to gather accurate data on electric and thermal energy for all gastronomy services. The energy provision on campus is centrally organised and therefore accessible. Only Canteen 2 purchases its own energy but provided energy bills.

For the analysis, the gastronomy services were divided into categories due to their different nature, related main products, and corresponding emission factors. The gastronomy categories consist of Stand, Café, and Canteen, described in

Table 2. Classification in and description of categories for the gastronomy services.

Category	Description
Stand	Food Stands with solely take-away food, outside
Café	Mostly coffee and tea with snacks but also breakfasts or lunch
Canteen	Cafeteria at the university, mainly for students and employees, meat/vegetarian/vegan options

The background colour of the table is for better readability, as the rows become clearer.

. Furthermore, the data were analysed based on the products and product groups. For the product groups, beef, pork, poultry, and fish were combined to the group meat. Butter, milk, and cheese were summarised to the product group dairy. Thus, a total of 19 products and 7 product groups were analysed.

In general, one needs to consider the different conditions for the gastronomy services, especially regarding the number of customers and their typical food products’ emission intensity but also factors such as the available customer area, opening hours, or customer throughput influence the amount of purchased energy and food products. Indeed, data about the number of customers were not available, as the services do not keep records on this. The size of the customer areas was asked but is somewhat misleading, as some gastronomy services only have takeaway food (Stands). In addition, cafes have a way faster customer throughput than restaurants and could therefore have more customers despite having less consumer area. Additionally, no correlation between the size of the GS and the energy use was found because of various influencing factors (e.g., building type, operation hours, utilisation). For these reasons, the size of the GS was not included in the analysis.

2.5. Budget Estimation

For the University of Graz emission budget for the third-party services, gastronomy services on and around campus were considered. On campus, only food products were included in the action field resources, since the share of energy of the gastronomy services is already included in the action field energy. In this article, we include the energy share in the results for a better comprehensive overview.

For the budget, an allocation factor was assigned since people from outside the university also consume the gastronomy services. This factor is 0.9 for both on-campus gastronomy and off-campus gastronomy, meaning that 90% of the customers—and 90% of the resulting GHG emissions—are assigned to the university [27].

Additionally, a fractional attribution factor with a value from 0.0 to 1.0 is used to express the institutional responsibility of the University of Graz. For the gastronomy around the campus (off-campus), a fractional attribution factor of 0.5 is used, meaning that the University is taking responsibility for 50% of the GHG emissions resulting from off-campus GS emissions, while the other 50% are assigned to the customers’ decision to consume near campus. For on-campus gastronomy, the University accepts institutional responsibility for the total GHG emissions, as they result only in the context of the existence of the University itself. Therefore, its fractional attribution factor is 1.0.

On campus, the data were collected as described in Section 2.4. We have data for electrical and thermal energy, as well as all food products from the list given in Table 1, for the three categories Stand, Café, and Canteen. However, the data for the food consumption of employees and students around the campus are simple estimates as of now. It was not possible to investigate the emissions of the GS around campus due to a lack of data and partially due to an unwillingness of the restaurant owners to participate. However, a comprehensive survey about the food consumption on and off campus is planned with employees and students. Its results will be adopted in version 2.0 of the CM reference budget 2020 [27].

First of all, we apply in Equation (4) the attribution factor 0.9 to the total food emissions budget. This is due to the possibility that part of the food consumptions is done by non-university employees or students. In Equation (5), we provide the estimate for the food consumption off-campus. We include here energy to the total GS emissions, since the energy emissions of the near campus restaurants have not yet been accounted for. The GS on campus were associated with 40% of the total gastronomy related emissions, and the remaining 60% were associated with the off-campus GS, since this is a reasonable assumption. We result in Equation (5) for the off-campus emissions, taking all the attribution factors into account:

$$GH{G}_{\mathit{on-campus}} \left[tC{O}_{2}eq\right]=E_{Food }\times 0.9$$

(4)

$$GH{G}_{\mathit{off-campus}} \left[tC{O}_{2}eq\right]=\frac{\left(E_{Food}+E_{Energy}\right)\times 0.9\times 0.6}{0.4}\times 0.5$$

(5)

3. Results

To provide an overview of the emissions per gastronomy service, the results were calculated for an average year. Therefore, the total emissions per available year per gastronomy service were summed up and divided by the number of available years per gastronomy service. Figure 1 displays the results for each GS per product group on the left-hand side, while the share of product groups on total emissions over all GS can be seen on the right-hand side. Stand 1 has the lowest annual emissions with 9.9 t CO₂eq. In comparison, Canteen 2 has the highest annual emissions with 87.8 t CO₂eq. As one can see, the range of results is vast (78 t CO₂eq). It is also noticeable that especially the proportion of meat varies greatly, being highest in Canteen 2, contributing to half of its total emissions.

Regarding energy, the differences are, amongst others, due to the energy intensity of devices, spatial conditions, or building type. Because of the many differences, the emissions also show a great variety between the different gastronomy services (12–74% of total emissions). Considering all gastronomy services in an average year, energy contributes to about 35% of total emissions.

On campus, the electricity and heating are centrally organised. Thermal energy is supplied from district heating. Since 2019, electricity originates solely from renewable sources (UZ46 certified). As the reference budget comprises the years 2015–2019, the effect in switching to renewable electricity is not significant for this budget but will impact the upcoming decade substantially. An exception is Canteen 2; although it is part of the university campus, it purchases its electricity externally yet also switched to renewable electricity in 2017.

While the emissions for electricity are distributed rather similarly, heating shows greater differences and contributes to a significant part of emissions (23–59%). Exceptions are Stands 1 and 2 as they use electric heating instead of thermal heating. Therefore, these emissions are assigned to electricity. For Canteen 2, the energy for heating originates from the universities’ solar panels. Because of the low emission factor for solar energy, solar heating contributes to 0.1% of total emissions and is therefore negligible.

The share of emissions for every product on total emissions per gastronomy service are presented in

Table 3. Share of emissions per product per GS in an average year (without the weighting factors from Equation (1)).

Product	Stand 1	Stand 2	Cafe 1	Cafe 2	Canteen 1	Canteen 2
Electricity	20.7%	11.9%	14.3%	19.8%	6.4%	19.4%
Heating	-	-	59.3%	22.9%	47.5%	0.1%
Beef	-	-	-	0.7%	0.1%	3.7%
Pork	32.2%	24.4%	-	8.8%	3.2%	23.5%
Chicken	20.9%	11.4%	-	-	2.2%	15.4%
Fish	-	-	-	0.3%	0.8%	7.1%
Fats & Oil	3.0%	-	0.1%	2.6%	1.6%	7.5%
Butter	-	8.6%	1.4%	3.5%	0.8%	1.2%
Cheese Mix	18.5%	27.4%	2.4%	9.2%	4.6%	5.8%
Milk	-	10.8%	13.7%	20.1%	22.4%	3.3%
Coffee	-	4.5%	8.7%	7.0%	7.0%	4.4%
Veg./Fruit	4.7%	0.9%	0.1%	5.1%	3.3%	8.5%

The background colour of the table is for better readability, as the rows become clearer.

. Having a closer look at specific food products, emissions depend on the assigned GS category, especially the share of dairy and meat products. In general, Stands 1 and 2 and Cafés 1 and 2 offer lower amounts and a lower variety of food products. Furthermore, it needs to be considered that Canteen 1 has the character of a café with lunch options. In comparison, Canteen 2 is one of the most common places to have lunch for university members.

To better understand the relation of food products and their corresponding emission factors, Figure 2 provides an overview of the share of the total amount of products in [t] and emissions in [t CO₂eq]. Here, one can see that, especially for vegetables and fruits, the amounts differ significantly. Although having a high share of vegetables in [t], the corresponding emissions are relatively low because of the lower emission factor. The opposite holds true for meat products, particularly for beef.

Summing up the values of the gastronomy services on campus result in an average budget 2015–2019 of 1016 t CO₂eq, including energy without weightings. Of this budget, almost 70% (694 t CO₂eq) belong to the two canteens. However, the calculation of the reference budget for the ICM project comprises allocation factors and weightings in its calculation. In addition, the energy for the budget is assigned to the action field energy and is therefore excluded from the above gastronomy budget. Accordingly, the reference budget for the gastronomy services (2015–2019) on campus results in emissions of 102 t CO₂eq, using Equation (4) for the calculation. Together with the estimation of the off-campus gastronomy from Equation (5), the total reference budget results in 211 t CO₂eq [27]. The entire reference budget 2020 for the University of Graz (including all four action fields) is 18,543 t CO₂eq [26,27]. Consequently, the emissions of third-party services in the action field resources (gastronomy services) account for 1.1% of total emissions.

4. Discussion

A GHG emissions inventory is a suitable method for calculating the emissions of an institution such as a university. However, because of limited time, resources, and data, the GS tool only considered energy and food products in the assessment. More information, such as other materials, packaging, or waste, is not included in the study. The gastronomy services also often had no records on this data. In terms of food products, it was decided to use the most commonly consumed food products instead of including every single product the GS purchased. This selection was made to simplify the process of data collection. Moreover, this study solely focuses on GHG emissions expressed in CO₂eq, although agriculture (production of food products) has wider ecological and social impacts such as land and water use or effects on human health [21].

Hence, the underlying method to estimate GHG emissions uses quantities of relevant products or activities and related emission factors. Products or activities represent, for example, the annual amount of electricity in [kWh] or the annual amount of used food products in [kg]. Emission factors give the mass of resulting emissions coming from the activity, e.g., in [g CO₂eq/kWh] or [g CO₂eq/kg]. This approach corresponds to the method used in the overall “ICM” project, where the estimate of gastronomy related GHG emissions is embedded. The methodological accordance ensures consistency and comparability.

As a simplification, the emission factors used did not distinguish between organic and non-organic products. In general, Lindenthal [30] argues that organic products are more environmentally friendly than conventional food products. The authors calculated a reduction potential of 10–35% per 1 kg of organic dairy, bread, or vegetables compared to the conventional products. However, in practice, food products are often purchased from various suppliers offering varying products, meaning that the food products are sometimes organic and sometimes not, which restrains data collection.

Overall, the results display the emission intensity of certain food products, especially within the food categories meat and dairy. Here, emission reductions are relatively easy implementable by increasing the amount of plant-based foods. Additionally, awareness campaigns and further information could raise the acceptance of changing offers for the customers. Moreover, the results indicate the difference in consuming renewable or non-renewable energy. Changing the energy source to renewable ones could significantly reduce emissions. Even so, the applicability and practicability need to be considered beforehand.

For testing the plausibility of the results, the results were compared to similar studies. In Graz, the University of Technology calculated about 200 tons of CO₂eq in 2017 for their Canteen [3]. The University of Klagenfurt calculated 214 t CO₂eq for their Canteen in 2015 [17]. The University of Natural Resources and Life Sciences (BOKU) in Vienna calculated 15 t CO₂eq in 2015 for one of their canteens, excluding energy [16]. All three universities use the ClimCalc tool for their emissions inventory for gastronomy services. This includes oils and fats, as well as the meat products beef, pork, chicken, and fish. Furthermore, they have their energy emissions included in their total values (200 t CO₂eq, 214 t CO₂eq), except the BOKU University in Vienna. Internationally, the studies show greater varieties in their scope. The University of Talca calculated a carbon footprint of 19 t CO₂eq for liquefied petroleum gas consumption in their dinner room (no inclusion of electricity and food products) in 2016 [5]. The Norwegian University of Technology and Science (NTNU) included servings of food (also meetings, etc.) as part of consumables in their carbon footprint of 2009. They attributed a carbon footprint 506 t CO₂eq for foods [12]. Furthermore, the Universidad Autonóma Metropolitana in Mexico City calculated 109 t CO₂eq for meat consumption (chicken, pork, beef, and fish) in their dining hall in 2016 [13]. With 139 t CO₂eq for the two university canteens (including energy) in an average year at the University of Graz, the results are considered as plausible.

In total, the emissions of the gastronomy services contribute to 1.1% of total emissions of the University of Graz. Although it contributes only a small proportion to total emissions, it was generally important to conduct this study for information purposes and to assess the share of emissions of gastronomy services within the university’s total GHG emissions. Only by looking at the holistic picture can validated reduction pathways be established. Furthermore, the University of Graz sees itself as responsible for its emissions and therefore also aims to gain comprehensive insights into all sub-areas of its institution. In addition, nutrition contributes significantly to the emissions of GHGs on an individual level: Czech, Danish, French, and Italian citizens emit on average between 1.9 and 2.2 t CO₂eq per year through their food consumption [31], and another study calculated 1.3 t CO₂eq per year for average Austrian citizens [32]. The University of Graz plans to integrate the following actions regarding their gastronomy services in order to employ reduction pathways towards a low carbon diet: only contracts with sustainable and regional suppliers, sustainable catering, and, in general, the reduction of meat at the Universities canteens.

Furthermore, we emphasize, that nutrition contributes significantly to the emissions of GHGs on a personal level. Therefore, the university has only limited direct control but can aim to promote a change in behaviour towards more plant-based, low-emission nutrition by raising awareness among employees and students. The changes to a more sustainable gastronomy and low-emission diets can therefore be seen as part of a wider change towards more environmentally friendly behaviour in general with the overall aim to meet the Paris climate goal.

5. Conclusions

As part of the Institutional Carbon Management project, this paper assessed the greenhouse gas emissions of the gastronomy services related to the campus of the University of Graz. Consequently, it contributes to the greenhouse gas emissions inventory and the calculation of a reference budget of the university, which is the basis for future reduction pathways, reaching net-zero emissions by 2030 and climate neutrality by 2040.

Despite limitations, the results indicate the high importance of energy and carbon-intensive food products on the emissions. Here, especially meat and dairy products contribute to the high emissions of foods. On average, vegetables, fruits, coffee, and fats and oil are relatively minor contributors. In total, the calculated reference budget 2020 from the years 2015–2019 results in emissions of 211 t CO₂eq, representing 1.1% of total emissions of the University of Graz.

Future research could use a more holistic approach to assess the GHG emissions of GS. More gastronomy services with a broader range of investigated data, such as machines, packaging materials, or food waste, could be part of a further study. Moreover, the GS tool could separate organic and non-organic products, leading to more customised emission factors. In addition, possible reduction potentials and mitigation pathways for GS could be developed. Moreover, a subsequent study is already planned to examine the university members’ customer behaviour to assign better the GHG emissions of the GS to the University of Graz.