GHG Emission Accounting and Reduction Strategies in the Academic Sector: A Case Study in Mexico

Abstract

The carbon footprint (CF) quantifies the greenhouse gas (GHG) emissions generated by human activities, expressed in carbon dioxide equivalent (CO₂e) units. It is an instrument for monitoring and mitigating the effects of climate change, which particularly affects low- and middle-income countries such as Mexico. The Mexican government has established a goal of reducing GHG emissions by 22% from the levels in 2000 by 2030. Although most efforts to reduce GHG emissions have been focused on the energy and agriculture sectors, the academic sector is also important since it can advise changes in public policy. In this study, the 2019 CF of the Centro Roberto Garza Sada (CRGS), a design school at the Universidad de Monterrey, was estimated in an effort to develop measures for reducing GHG emissions. The GHG Protocol was employed to calculate the total CF of the CRGS and identify the greatest contributors, including commuting (50.2%), energy purchase (28.5%), business travel (19.6%), and energy generation, use of paper, refrigerants, and shipments (1.7%). Three progressive mitigation scenarios were developed to reduce the GHG emissions from commuting, energy consumption, collaborators and student mobility, and material resources. These strategies could reduce the GHG emissions of the CRGS by 63.5% of the baseline assessed.

1. Introduction

Climate change (CC) is considered one of the biggest problems currently facing humanity and is defined as the long-term variation of climatic parameters such as temperature, rainfall, and air velocity compared with global averages recorded over the past century [1]. The significance of CC began to gain relevance with the recognition of the relationship between greenhouse gas (GHG) emissions, particularly carbon dioxide (CO₂), and global warming (GW). Most GHG emissions have natural sources, but the increase in recent centuries is in parallel to the increase in anthropogenic activities [2]. Meanwhile, the air temperature has increased by 0.5–1 °C in the last 150 years [3]. Keeping the temperature increase below 2 °C is important because of the irreversible consequences, such as the melting of ice masses that would increase the sea level by approximately 70 m, increased hurricane intensity, and abrupt climate changes [3]. Over past decades, significant international actions have been established to minimize the effects of CC: the Kyoto Protocol aimed at reducing GHG emissions [4], the Copenhagen Accord [5] was designed to lower GHG emissions to limit the temperature rise below 2 °C, and the Paris Agreement [6] to document GHG emissions. Accordingly, monitoring GHG emissions has become increasingly relevant, which has led to an interest in developing instruments for their quantification. One of the main tools related to CC is the carbon footprint (CF) estimation, which refers to the amount of GHG emissions, expressed in units of CO₂ equivalent (CO₂e), that are directly or indirectly generated throughout the life cycle of a product, activity, service, or organization [7]. CF is an instrument that contributes to monitoring and mitigating the impact of climate change (CC), which mainly affects low- and middle-income countries such as Mexico. In the country, CC has the potential to impact 15% of the territory, 68% of the population, and 71% of the GDP (gross domestic product). In response, the Mexican government has implemented a comprehensive climate change policy that aims to mitigate GHG emissions, adapting to the impacts of CC, and promote sustainable development. The country has set ambitious targets, including a commitment to reduce its emissions by 22% by 2030 and increase the share of renewable energy in its electricity generation. Mexico has also implemented various initiatives to promote energy efficiency, sustainable transportation, and reforestation. Furthermore, the government has established a National Climate Change Strategy and enacted a climate change law to guide its actions. In addition, Mexico is actively engaged in international climate negotiations, collaborating with other nations to address the global challenge of climate change [3,6]. In Mexico, the main sources of GHG emissions are energy (67%), agriculture and livestock (12%), industrial processes (8%), waste (6%), and land use (6%) [3]. Among GHGs, CO₂ is dominant and comprises 71% of national emissions [8].

Although academic institutions do not contribute significantly to GHG emissions at the national level, they are essential actors in their reduction in two important ways: by helping advise environmental policy and by serving as a reference for organizations from different sectors to apply similar mechanisms or systems to reduce GHG emissions. Many universities and research centers worldwide have reported their own CFs and implemented various reduction measures to realize a sustainable development approach that is both environmentally and economically beneficial [9]. In 2010, the University of Sydney in Australia assessed its environmental performance in terms of its CF generated by the energy consumption of its various faculties. This evaluation was complemented by an economic analysis of mitigation scenarios, which concluded that one of the main obstacles to sustainable development is the lack of financing to implement specific proposals [10]. In Mexico, the Engineering Institute of the Universidad Nacional Autónoma de México (UNAM) estimated its CF for 2010 according to objectives established in its sustainable development plan. The institute reported that 50% of its GHG emissions were attributed to commuting and 42% from energy consumption [11]. Similarly, in 2016, the Universidad de Guadalajara (UDG) evaluated their university centers and estimated a CF, identifying energy consumption as the main contributor, and direct GHG emissions from fuel combustion and refrigeration [12].

The Centro Roberto Garza Sada (CRGS) building is in Monterrey, Nuevo León, Mexico, and houses the Architecture and Habitat Sciences and Art and Design Schools of the Universidad de Monterrey (UDEM). It is considered an architectural landmark at an international level, and it is the headquarters for the training, creation, and preservation of art, architecture, and design in Latin America [13]. In 2019, the CRGS had a population of 1780 people: 1600 students and 180 employees, including administration, professors, researchers, and staff in janitorial and maintenance positions. With an area of 13,000 m² distributed over six floors, the CRGS comprises classrooms, workshops, open spaces, multipurpose rooms, and offices. It received the LEED Silver Construction certification from the US Green Building Council in 2014 [13], which involved a series of reviews on its sustainability, from the type of building materials used to the efficiency of the building during operation. The CRGS is a hallmark of UDEM’s 2030 vision, which aims to foster a sustainable environment both on and off campus. UDEM is currently implementing initiatives focused on the sustainable development of its operation and curriculum, including urban mobility strategies, comprehensive solid waste management, and energy efficiency evaluation through the Sustainability Tracking, Assessment & Rating System [14]. The objective of the present study was to estimate the 2019 CF of the CRGS and develop mitigation scenarios to support UDEM and the academic sector as a whole in promoting sustainable development at the national level. The selection of this year allowed for establishing a baseline for GHG emissions quantification in subsequent years and represented the normal operational conditions before the onset of the COVID-19 pandemic.

2. Materials and Methods

This study followed the GHG Protocol [15], which is a globally recognized standard for measuring and managing GHG emissions from companies and/or organizations. The GHG Protocol has three scopes: (1) direct emissions from sources that are owned or controlled by the organization; (2) indirect emissions from the generation of purchased electricity, heat, or steam consumed by the organization; and (3) indirect emissions that are a consequence of activities by the organization but are from sources not owned or controlled by the organization [16]. Considering that the CF is remarkable only when all three scopes are included, the quantification of Scope 3 has increased its relevance [17]. However, Scope 3 emissions are often complex to calculate because they are primarily based on behavioral patterns [18].

Table 1. Categories suggested from GHG Protocol considered within this study.

Scope	Category	Description	Included in CF Estimation
1	Company facilities	Power generation	Yes
	Company vehicles	No owner vehicles	NA
2	Purchased electricity	Power purchase	Yes
3	Purchased goods and services	Upstream emissions of purchased goods and services	No
	Capital goods (paper)	Paper | Downstream emissions of purchased paper	Yes
	Fuel and energy-related activity	Energy not included in scope 1 or 2	NA
	Commuting	Trips by the community to and from the university	Yes
		Visitor travel	No
	Distribution (shipments)	Downstream transportation and distribution of inputs	Yes
	Waste	Negligible amount of organic waste	No
	Business travel	Travel by community	Yes
		Travel by visitors	No
	Leased assets	Operation of leased assets	NA

NA: Not applicable.

shows the limits of the CF in terms of the categories included in each scope established by the GHG Protocol and that were considered in this study. The purchased goods and services category was not considered because services such as waste collection, gardening, and purchases are managed at the university level, not at the CRGS. On the other hand, the categories of transportation and visitor trips were not considered because CRGS does not assume the cost, and the number of visiting professors is limited throughout the year. Finally, the waste category was not considered due to the negligible amount of organic waste generated by the CRGS since this center does not include a dining area. To calculate the GHG emissions of a category, two types of data were required: activity data (AD), which is the quantitative measure of an activity that results in GHG emissions; and the emission factor (EF), which converts AD into GHG emissions. The Intergovernmental Panel on Climate Change [19] considers an inventory as tier 3 of superior quality when the AD is based on primary data and the EF is specific to each category at a national/regional level [8]. This study met these requirements.

2.1. Energy Generation

This category includes direct emissions from electricity generated by the emergency plant of the CRGS, which uses diesel fuel. This plant is activated monthly for maintenance and is operated when any power outage or unforeseen event occurs. The AD comprised the amount of diesel consumed by the emergency plant during 2019, which was set as the base year (BY). The maintenance consumed 2.22 L of diesel each month. In May, two events occurred (unforeseen and programmed) that consumed 549 L of diesel, which resulted in a total consumption of 573 L.

2.2. Energy Purchase

This category includes the indirect emissions from the purchase of electricity from the Federal Electricity Commission (CFE) for the daily operation of the CRGS. The AD is measured as the megawatt-hours purchased during the BY (Figure 1). The months with the highest electricity consumption were May, June, August, September, and October, which are the hottest months of the year and thus relied heavily on air conditioning. The electricity consumption was lower in July because most of the students were on summer break. In total, the CRGS consumed 1178 MWh during the BY.

2.3. Commuting

This category includes the indirect emissions resulting from the CRGS community (i.e., students and collaborators) commuting between their homes and the university (i.e., round-trip distance). The AD is composed of the amount of fuel consumption (i.e., gasoline and diesel). The Mobility Department and School Planning Department of UDEM provided information about the CRGS students such as the main means of transportation used and their postal codes, which were used to estimate the distance between their homes and the university. The information on CRGS collaborators was obtained via administrative coordination and surveying them directly. The collected information was used to calculate the annual distance traveled by individuals (km). The distance was then correlated with the performance of their means of transportation (km/L) to obtain the fuel consumption (L) (see Supplementary Material).

Table 2. Motorized means of transportation considered for calculating the CF of the CRGS.

Means of Transportation	Description
Private car	Used by those who commuted alone in their own car (1 passenger per trip)
Carpool	Vehicle shared either as a driver or passenger (2 passengers per trip)
Taxi or similar	Private transportation service (1 passenger per trip)
Public transportation (bus)	Public bus (29 passengers per trip) *
University’s vanpooling service	University’s free-private transportation service (2 passengers per trip)
University’s bus service	University’s private and paid transportation service (29 passengers per trip) *

* (Metrobús and CETRAM, 2014) [20].

presents the motorized means of transportation considered in this study.

2.4. Business Travel

This category includes the indirect emissions from domestic and international air travel by the CRGS community. The AD is measured in terms of kilometers traveled (km). Information was collected from the Academic Mobility Department on the destination cities of trips made by students and collaborators. During the BY, 442 economy class round trips were made (43 domestic and 399 international) with a total distance traveled of 5,548,178 km. The distance traveled was calculated using the International Civil Aviation Organization’s Emissions Calculator [21], which also assigns the type and amount of fuel consumed and load factor for each aircraft.

2.5. Shipments

This category includes the indirect emissions from domestic and international shipments made by CRGS collaborators. The AD is composed of the diesel consumption (L) for land shipments and distance traveled (km) for air shipments. The information was provided by the University’s Messenger Department. During the BY, 26 package shipments were sent by collaborators, of which seven had destinations abroad. On average, the packages weighed of 0.4 kg. For domestic shipments, the fuel consumption per kilometer was calculated considering the load factor. For international shipments, the Emissions Calculator [21] was used to estimate the distance traveled by each shipment, where a package was considered a passenger. In total, 0.9 L of fuel were consumed on land routes, and 50,604 km were traveled by air for CGRS to receive shipments.

2.6. Paper Purchase

This category includes the indirect emissions from purchasing paper for printing in the CRGS. The AD is made up of the virgin bond paper purchased (kg). The Purchasing Department indicated that 5740 kg of paper were purchased in total.

2.7. Refrigerants

This category includes the indirect emissions resulting from refrigerant leakage from the heating, ventilation, and air conditioning (HVAC) of the CRGS. No information was available for this category; therefore, IPCC guidelines were used to calculate the GHG emissions [22]. In the BY, six units or 11.35 kg of R410-A refrigerant gas were used to supply the HVAC system. R410-A is composed of HFC-32 and HFC-125, and it has a GW potential of 2090 [22]. According to the IPCC [22], 10% of all equipment leaks. Therefore, the GHG emissions generated by refrigeration were calculated by using the equation suggested on the IPCC [22]:

Elifetime,t = Bt × (x/100)

(1)

where Elifetime,t is the HFC emissions from system operation in year t (kg), Bt is the HFC emissions in existing sub-application banks in year t (kg), and x is the annual rate of HFC emissions (i.e., EF) from each sub-application bank during operation accounting for the average annual leakage and emissions during maintenance (%).

3. Results

3.1. Overall Results

In the BY, the CRGS had a CF of 2089 t-CO₂e. Scopes 1, 2, and 3 comprised 0.07% (1.5 t-CO₂e), 28.45% (594.5 t-CO₂e), and 71.48% (1493 t-CO₂e), respectively, of the CF.

Table 3. Summary of GHG emissions by the CRGS in the BY.

Scope	Category	Subcategory	Data Activity		Emission Factor		Emissions by Subcategory		Emissions by Category		Emissions by Scope
			Description	Amount	Amount	Unit	t-CO2e	%	t-CO2e	%	t-CO2e	%
1	Energy generation	-	Diesel consumption (L)	573.33	2.614	kg-CO2e/L [24]	1.50	0.07	1.50	0.07	1.50	0.07
2	Energy purchase	-	Purchased energy (MWh)	1178.00	0.505	t-CO2e/MWh [25]	594.39	28.45	594.39	28.45	594.39	28.45
3	Commuting	Private car	Gasoline consumption (L)	326,331.00	2.489	kg-CO2e/L [24]	812.24	38.87	1048.26	50.17	1493.54	71.48
		Carpool	Gasoline consumption (L)	28,401.00	2.489	kg-CO2e/L [24]	70.69	3.38
		* Uber/taxi or similar	Gasoline consumption (L)	23,339.00	2.489	kg-CO2e/L [24]	58.09	2.78
		Public transportation (bus)	Diesel consumption (L)	13,517.00	2.614	kg-CO2e/L [24]	35.33	1.69
		University’s vanpooling service	Diesel consumption (L)	19,301.00	2.614	kg-CO2e/L [24]	50.45	2.41
		University’s bus service	Diesel consumption (L)	8208.00	2.614	kg-CO2e/L [24]	21.46	1.03
	Business travel	Air travel	Distance traveled (km)	5,548,178.00	0.074	kg-CO2e/pass-km [21]	409.94	19.62	409.94	19.62
	Shipments	Land shipments	Magna fuel consumption (L)	0.87	2.489	kg-CO2e/L [24]	0.00	0.00	3.89	0.19
		Air shipments	Distance traveled (km)	50,604.00	0.077	kg-CO2e/kg-km [24]	3.89	0.19
	Paper	-	Virgin bond paper (kg)	5740.00	3.000	kg-CO2e/kg [26]	17.22	0.82	17.22	0.82
	Refrigerants	-	Refrigerant (kg)	68.10	2090	GWP [22]	14.23	0.68	14.23	0.68
	Total CF						2089.42	100	2089.42	100	2089.42	100

* A commuting network company that offers ride-sharing services through its mobile application.

lists the contributions of each category. Three categories comprised 98.24% of the CF: energy purchase, commuting, and business travel. All of these are predominantly indirect emissions. The private car as a means of transportation was responsible for about 39% of the total CF, so it may be considered a key point for mitigation measures. Energy purchases represented 28% of the total CF because about 88% of the energy production in Mexico in the BY came from fossil fuels [23]. Internationalization is paramount to UDEM, which may explain why business travel had the third-highest contribution to the CF at 19.6%. A more extensive analysis of these categories is presented in the following subsections.

3.2. Energy Purchase

The energy to meet the CRGS’s demand was estimated by the CFE considering Mexico’s electricity mix, which is composed of 59.83% oil, 23.15% natural gas, 5.7% biomass, 3.64 coal, 3.37% geothermal power, wind, and solar, 1.34% hydropower, 1.97% nuclear, and 0.96% other sources [23]. The CRGS has implemented various strategies for efficient energy use, including construction design, motion sensors, automatic blinds, double-glazed windows, low-consumption lighting fixtures, and MERV-13 filters for air conditioning and ventilation. These actions allowed the CRGS to achieve a LEED Platinum certification by reducing energy consumption by 17.3% compared to similar buildings according to ASHRAE/IESNA Standard 90.1–2004 [27]. This reduction was achieved by a 35% reduction in the installed air conditioning capacity, a 55% reduction in electricity consumption by using low-consumption illumination, and MERV-13 filters with 94.6% filtration efficiency for air conditioning and ventilation [27]. An energy census was performed, which indicated that the largest consumers of energy were equipment (14%), illumination (18%), elevators (8%), and HVAC (59%).

3.3. Commuting

Among the categories, commuting had the highest contribution to the CF with 1048.3 t-CO₂e representing 50.17% of the total.

Table 4. Means of transportation used by the CRGS community.

Means of Transport	Distribution of Use	Emission Factor * (kg-CO2e/km-Passenger)
Private car	58%	0.17
Uber/taxi or similar	10%	0.17
Carpool	10%	0.08
Public bus	6%	0.05
University’s vanpooling service	10%	0.14
University’s bus service	3%	0.05
Bicycle	2%	0
Walking	1%	0

* EF was calculated with the primary data obtained from UDEM’s Mobility Department (see Supplementary Material).

presents the distribution of usage and EF (kg-CO₂e/km-passenger) of each means of transportation for the CRGS community. The most-used modes of transportation were private cars, which also presented the highest EF, thus making it the most polluting conveyance within the CRGS community. Uber, taxi, or similar services share the same EF; however, the community utilizes these modes of transportation less frequently. Since this transportation style does not discriminate if people commute alone or accompanied, its GHG emissions cannot be attributed to any people unit. In contrast, the GHG emissions for each trip by a public bus were divided among the 29 passengers onboard. According to Versteijlen et al. [28], the choice of transportation is influenced by financial factors, limited alternatives, and psychological factors. The following factors may affect the decision to use private cars as the main means of transportation:

Comfort: because of the warm weather of the city of Monterrey, which can reach up to 40 °C (104 °F), people prefer to travel by air-conditioned car rather than by public transportation, which rarely has air conditioning [29].

Distance and travel time: the average travel distance between UDEM and students’ homes is 13 km. Because of the expansion of the metropolitan area, the fastest means of transportation is a private car, with an average time of 60 min/roundtrip compared with 89 min/roundtrip using public transportation [29].

Average ownership of private vehicles: In the BY, the National Institute of Statistic and Geography (INEGI) registered 1.75 million private cars in circulation in the state of Nuevo Leon, which has a population of about 5.65 million. This translates to 0.31 private cars per person, as opposed to 0.27 private cars per person at the national level. In the BY, 58% of the CRGS community traveled to the campus by private car.

Cost of Uber/taxi or similar: Although this service is as fast as a private car, it has a higher cost compared with the cost of fuel per trip by private car.

Matching schedules for carpooling: The use of carpooling depends on matching schedules with other members of the CRGS community.

Quality of public transportation: The metropolitan area does not have an efficient transportation system. The subway only has three connecting lines, none of which are near UDEM. Around 73% of citizens do not use public transportation because of the poor quality of the service [29].

Coverage and service hours of the university’s vanpooling service: the coverage of this service is limited to certain routes close to the university covering 48.3 km within a radius of 5.7 km from UDEM. In contrast to students, collaborators do not have flexible schedules that can be adapted to those of this transportation service.

Time, cost, and service hours of the university’s bus service: Having a larger coverage area means longer trips. In addition to the high cost of this service, the available hours are limited, making it unsuitable for most of the CRGS community.

Public roads: Walking and bicycles are the least frequently used means of transportation because of Monterrey’s climate, geography, and lack of infrastructure with 26 km of bicycle lanes compared with 322 km in Mexico City [29].

3.4. Business Travel

This category contributed 19% of the total CF. Air travel by the CRGS community consisted of trips to 69 cities in 32 countries, mainly in America and Europe. The main reason for travel was international student exchanges and conferences by collaborators. Of the total trips made, 381 were by students (5,090,410 km), and 61 were by academic collaborators (457,768 km). On average, each passenger emitted 0.167 g-CO₂/km. The average GHG emissions per round-trip were 0.92 t-CO₂e/journey. This indicator is important for establishing emission compensation strategies through a green fund.

4. Discussion

4.1. Normalized Results

In the BY, the CF per capita of the CRGS community was 1.17 t-CO₂e/person considering Scopes 1–3. The CF per unit area was 0.05 t-CO₂e/m² considering Scopes 1 and 2. When the results were normalized for students and collaborators (i.e., teachers, researchers, administrative staff, and service staff), expected differences showed up (

Table 5. CF per capita among members of the CRGS community.

CRGS Community	Total CF (t-CO2e) *	CF per Capita (t-CO2e) *
Students	1814.4	1.13
Collaborators (teachers, researchers, and administrative staff)	248.7	1.64
Collaborators (service staff)	26.5	0.91
Total	2089.5	1.17

* Base year (2019).

). As a group, the teachers, researchers, and administrative staff had the highest CF per capita due to their main means of transportation (i.e., private cars) and travel distances. The service staff had the lowest CF per capita because their main means of transportation was by public bus.

4.1.1. CF per Capita

The average CF per capita is 7.2 t-CO₂e/year in Mexico [3]. Thus, the GHG emissions generated by activities related to the CRGS represent approximately 18% of the CF for each member of the CRGS community. This result is like the case study of II UNAM, which found that an employee generated 21% of their CF on average during academic activities [11]. If the results are narrowed to the city level, the average citizen of Monterrey has an average CF per capita of 4.1 t-CO₂e/year based on consumption patterns and purchasing power [30]. Therefore, the GHG emissions generated by academic activities represent approximately 28% of the CF of members of the CRGS community. This is similar to the results reported by the Norwegian University of Technology and Science (NTUN), which also found that academic activities represented about 30% of the CF of students [31]. Depending on the characteristics of the region, academic activities can represent a higher proportion; considering that the CF per capita for Mexico City is 4.1 t-CO₂/e, the case study for II UNAM showed that academic activities represented 52% of the personal CF of their community members [11].

4.1.2. CF per Unit Area

The CF per unit area reflects the building performance of the CRGS, and it was 0.05 t-CO₂e/m² according to Scopes 1 and 2. Klein-Banai and Theis [32] evaluated 29 colleges and universities in the USA and found that the average CF per unit area was close to 0.11 t-CO₂e/m². The difference can mainly be attributed to the energy-saving design of the CRGS, but other factors include the number of heating and cooling degree days (HCDDs) and the fuel used: coal for cooling and natural gas for heating. In their study, the universities in the USA had a larger number of heating degree days (HDDs) because of their geographic location. Similar results were obtained when the CF per unit area was compared with other studies within Mexico. For example, II UNAM had a CF per unit area of 0.03 t-CO₂e/m² [11]. The higher CF per unit area for CRGS can be attributed to its HVAC system, which contributed 60% of the total electricity consumption and had to cool six levels with double the height of the building in II UNAM (5.40 m). The electricity consumption of the HVAC system can be related to the climate. Monterrey has warm weather, and it had 3073 h of annual thermal discomfort during the BY: 2442 h for cooling and 631 h for heating [33].

Klein-Banai and Theis [32] found that 65% of the GHG emissions generated by universities in the USA can be attributed to Scopes 1 and 2. This differs from the results in this study, which found that most GHG emissions by the CRGS were in Scope 3. This can be attributed to the means of transportation used for commuting and business travel, which together were responsible for 70% of the total CF.

Table 6. CFs of academic institutions around the world.

Case Study			CF Indicators t-CO2e
University	Country	Method *	Per Capita	Square Meter	Overall	Scope 1 (%)	Scope 2 (%)	Scope 3 (%)
UIC [37]	USA	GHG Protocol	3.6	0.2	275,000	64	17	19
LSU [38]	USA	GHG Protocol	6.1	0.16	162,742	57	18	25
DMU [36]	UK	GHG Protocol	1.99	0.35	51,080	6	15	79
NTNU [31]	Norway	EEIOA	4.6	0.03	92,100	19	0	81
MU [42]	Thailand	LCA	-	-	1092	1	80	20
BITS [39]	India	LCA	4.65	12.01	16,500	1	50	49
UTADEO [34]	Colombia	ISO 14064-1	0.15	0.52	1688	8	37	55
EI PUCC [35]	Chile	GHG Protocol	0.66	0.1	3292	1	41	58
II UNAM [11]	Mexico	GHG Protocol	1.46	0.06	1577	5	41	53
UDG [12]	Mexico	ISO 14064-1	0.14	-	9747	21	55	23
Present study CRGS	Mexico	GHG Protocol	1.17	0.05	2090	0.10%	28.50%	71.50%

* Methodology used to calculate HC.

shows the main parameters considered for estimating the CF of universities around the world, including the present study. Most of the case studies had a distribution of GHG emissions dominated by Scope 3 and followed by Scope 2. This was the case for Latin American universities [11,12,34,35], which had no or little onsite electricity generation. These universities had a lower CF per capita than Asian, European, and North American universities because of the relation among the student population and diverse size of facilities (onsite power generation and pilot plants, wind tunnels, furnaces, and cryogenic facilities). For instance, the European and American universities were larger and required more energy to supply their needs. De Montfort University (DMU), UK, and Norwegian University of Technology and Science (NTNU), Norway [31,36], had greater contributions to their CF from direct emissions (i.e., Scope 1) compared to the rest of the case studies. The University of Illinois in Chicago (UIC) and Louisiana State University (LSU) in North America [37,38] had larger contributions from Scope 1, followed by Scope 3. In contrast, Asian universities had larger contributions from Scope 2, followed by Scope 3. In the case of India, most of the GHG emissions could be attributed to electricity consumption, as coal contributes up to 60% of the national energy mix [39]. This problem is shared by developing countries, whose economic, industrial, and demographic growth depends on more accessible and cheaper fossil fuels. According to Nair et al. [40], developing countries must seek a balance between short- and long-term economic growth while transitioning to an economy less dependent on carbon. Furthermore, universities in developed countries had a larger CF per capita than their counterparts in Latin America. Ritchie [41] demonstrated that this may be explained by the strong connection between income and CF per capita, where CO₂ emissions increase with the standard of living. Regarding the CRGS, a comparison can be made with the Pontifical Catholic University of Chile (PUCC). This university has a population 2.8 times larger than the CRGS, and it reported a CF per capita of 0.66 t-CO₂e. Its CF per unit area doubled that of CRGS, which can mainly be attributed to its energy use. PUCC also reported that GHG emissions from international travel were 27 times lower than those generated by the CRGS.

4.2. Comparison of GHG Emissions by Category

Comparisons between the CFs of different universities present considerable challenges due to the significant variations in their respective profiles, such as the distinction between teaching-oriented and research-oriented institutions, or those emphasizing science and engineering versus arts disciplines. Furthermore, comparisons are further complicated by the divergence in methodologies universities employ to estimate their carbon footprints, particularly regarding the activities considered within the Scope 3 category. Despite these inherent difficulties, comparisons across universities have nonetheless been undertaken because they provide valuable contextual understanding for the study. II UNAM [11] and DMU [36] had CFs per capita comparable to the CRGS results. Despite the similar CFs per capita, the distribution among scopes varied considerably. The most notable difference was observed for Scope 1 because the CRGS generates an insignificant amount of energy on site (0.1% of total GHG emissions). In contrast, DMU and II UNAM reported 6% and 5% of GHG emissions corresponding to Scope 1 owing to fuel consumption by their vehicle fleets and natural gas combustion for space heating, respectively. The CRGS does not have its own vehicle fleet and has minimal heating demand. Regarding GHG emissions from energy purchased (Scope 2), CRGS and II UNAM reported values of 0.33 and 0.68 t-CO₂e, respectively, for a difference of 50%. According to Güereca et al. [11], the high energy consumption at II UNAM can be attributed to the type of activities carried out by the institute. Applied research at II UNAM makes use of laboratories and equipment that are energy intensive. Within Scope 3, the difference in GHG emissions can be attributed to business travel; the CRGS reported GHG emissions 2.5 times more than that of DMU and four times more than that of II UNAM, even though II UNAM made 24 more trips in America. For DMU [36], the business travel category represented 7.9% of the total CF and mainly comprised student and business trips. This difference in results is mainly because 40% of the trips made by the CRGS community took place outside the continent. By population type, student trips represented 17.8%, 5.8%, and 0.5% of the total CF for the CRGS, DMU, and II UNAM, respectively. The CRGS and II UNAM had similar GHG emissions in the commuting category at 50% and 45%, respectively, of the total CF, but the contribution was only 21% for DMU. Although DMU reported 79% of its CF was in Scope 3 and CRGS reported 71%, the former considered GHG emissions from construction, product manufacturing, and food services, which were not considered in the present study.

CFs can be used to identify hotspots in supply chains [18]. However, although the distribution by scope is useful for locating the main sources of GHG emissions, it is difficult to establish a comparison based on these percentages because each study accounted for different activities, especially in Scope 3. According to Lenzen and Treloar [43], process analyses may not be comparable when the boundaries between systems are different. Larsen and Hertwich [18] supported this argument by suggesting that a framework based on methodological coherence needs to be established to define the universal organizational limits to which universities must adhere.

4.3. Mitigation Scenarios

In support of the Race to Zero movement [44], Mexican universities have committed to achieving net-zero GHG emissions before 2050, along with more than 550 universities worldwide. They have also committed to mobilizing resources for CC research and increasing the role of environmental education in study and outreach programs on campus. To contribute to this vision, three mitigation scenarios are proposed for reducing the CF of the CRGS in the short, medium, and long term, prioritizing those sources with greater potential for reducing GHG emissions.

Scenario 1: This scenario considers strategies for reducing GHG emissions in the energy purchase, commuting, and business travel categories.

Reduce the amount of energy purchased: The roof dimensions of the CRGS make it possible to install a photovoltaic system capable of supplying 27% of the electricity demand according to simulations carried out with System Advisory Model (SAM) software (version 2020.2.29) [45].

Allow students to attend online classes 2 out of 5 days per week instead of going to the campus for a course distribution of 40% online and 60% face-to-face: Before the COVID-19 pandemic, most courses were face-to-face, so students traveled to the campus 4 days a week on average. Having experienced an entire semester online, students have found benefits in online classes, such as flexibility, convenience, cost reduction in transportation, and time for engaging in other activities. It is worth mentioning that not all courses offered at CRGS can be conducted online as they require students’ presence, such as practical studio art courses. However, a considerable number of classes can be taken in an online format. Migrating courses to a hybrid schedule of 30–79% online and face-to-face activities [28] would reduce the total CF by 23%.

Reduce trips by administrative staff, collaborators, and teachers by 19%: According to the Wall Street Journal [46], 19–36% of travel demand for administrative staff is expected to disappear post-COVID-19. In addition, an internal emission compensation program can be created to compensate for 100% of the GHG emissions generated by the air travel of collaborators that cannot be avoided. This aligns with the results of Geneidy et al. [47], who studied the importance of organizational policies encouraging employees to choose greener means of transportation. Another step would be to apply a fee to each flight to offset 30% of the GHG emissions due to student travel, considering a cost of USD 18.50 to offset 1 t-CO₂ [48]. According to Lu and Wang [49], only 5% of those surveyed demonstrated an understanding of the environmental impacts of air travel, and young travelers between 21 and 30 years of age showed little willingness to change air travel habits, such as preferring land travel or video conferences. Only passengers over 50 years old were willing to participate in a project to offset GHG emissions from air travel. However, the willingness changed positively when travelers made trips once a year.

Scenario 2. This scenario considers additional strategies that are meant to be implemented after Scenario 1 is implemented.

In line with the national goal for 2024, purchase 35% of the energy demand in the BY from renewable energy sources [50]: Although this percentage can be quite ambitious, it is possible to achieve based on precedent: UDEM bought 60% of its energy from low-carbon sources through cogeneration in 2015–2018.

Reduce in-person attendance by collaborators and replace it with home offices: Before the pandemic, employees attended the campus 5 days a week. A new normality is proposed where employees attend on average 3.5 days of face-to-face activities (3 days for collaborators with essential non-face-to-face activities and 4 days for collaborators with face-to-face activities). In a post-COVID-19 world, Sharfuddin [51] and Geneidy et al. [47] estimate that organizations will implement a hybrid scheme of 60% face-to-face activities and 40% virtual work (i.e., being present three out of five working days per week). This step will reduce the energy demand of the workspace, but more significantly, it will reduce the need to commute.

Reduce private car usage among students by up to 23% through the implementation of travel demand management (TDM) policies. Strategies such as increasing parking costs and promoting carpooling and vanpooling services can be effective. The revenue generated from increased parking fees can be allocated to fund sustainable transportation initiatives, including bike infrastructure improvements, subsidized transit or bus passes, discounted parking rates for shared vehicles, and carpooling programs. This percentage was obtained from data on using the vanpooling service and carpool parking spaces [52]; it represents half of the students that use private cars and live near the stops of the vanpooling service or other students who can change their means of transportation. Barata et al. [53] described how providing parking space in universities drives the use of private transportation. Increasing parking prices tends to deter the use of cars and increases the use of public transportation [54,55]. In places with limited public transportation services, charging for parking may incentivize carpooling [56]. In contrast, Crotti et al. [57] demonstrated that free parking permits increase the use of private cars and potentially carpooling options. Thus, parking costs should only be increased for solo drivers, while free parking should be provided for carpooling. Although UDEM already has an exclusive parking lot for carpooling, it is extremely limited. It has 105 parking spaces for a community of around 10,000 undergraduate and graduate students and collaborators and is 0.5 km from the CRGS. Hence, the CRGS can promote carpooling by reserving 130 parking spaces in the nearest parking lot for carpooling without charging. The university vanpooling service can be promoted by increasing awareness about the routes, stops, and environmental benefits. Promoting this service is important because only two people on average were transported by each van in the BY, despite a capacity of 20 seats.

Strengthen the voluntary emission compensation program for student trips to offset an extra 20% of the GHG emissions from travel: This can be achieved through institutional policy and promoting awareness because young people who are educated and aware of the environment are more willing to pay more to reduce their CF [58].

Scenario 3. This scenario considers additional strategies that are meant to be implemented after Scenario 2 is implemented.

Increase the purchase of clean energy by an additional 8% to reach 43% of the demand in the base scenario: This is in line with the national goal for 2030 [50]. Reduce the number of students using private cars for transportation by a further 23%: TDM policies can be promoted such as increasing parking costs [59], which can serve as a subsidy for the promotion of carpooling and vanpooling services and the use of bicycles. Ensuring a bicycle-friendly environment on and around the campus may encourage students and collaborators to use this means of transportation [57].

Apply a fee to every flight to offset 20% of the GHG emissions from travel: This will reach 90% compensation for student trips. Following the examples of Yale University [60] and UCLA [61], the emission compensation fund can be allocated to internal projects of the university to realize net-zero emissions, such as investment in more clean energy, sustainable mobility plans, and sustainability research.

As shown in Figure 2, implementing these three scenarios through community participation would reduce the total CF of the CRGS by 63.5%. The three scenarios are meant for gradual implementation at a pace the CRGS community can accommodate. To increase the number of categories considered in the study, the following actions are proposed: in the upstream emissions category of purchased goods and services, it is suggested to differentiate products for each of the different schools of the university and measure the waste generation from green areas by CRGS to allocate the corresponding GHG emissions. Likewise, in the visitor travel category, it is proposed to implement a visitor registration form, collecting the routes and modes of transportation used by the invited collaborator. A monitoring committee should be established to ensure the correct execution of the strategies and measure their impact over the years.

According to Li et al. [62], research on the carbon footprints of higher education institutions places decarbonization in a position to help solve climate issues [9], encourage sustainable consumption patterns [63], promote a low-carbon economy [64], and develop low-carbon energy sources [65]. Institutions of higher education are of utmost importance to achieve global net-zero. They help educate and raise awareness among future leaders about environmental impacts, create platforms for dialogue, and promote public and inclusive policies in sociopolitical and economic regimes. Their influence in other sectors is essential because CC is a challenge that requires global and immediate action to curb its effects. Creative and immediate solutions must be developed by institutes of higher education to offset the negative impact of GHG emissions on the environment [66]. Over the years, universities have strengthened their efforts to document and evaluate their CFs, which can be used to generate and demonstrate tools for decarbonization [67,68,69]. To be leaders in sustainability and drivers of change, universities must ensure that their students understand the need to guarantee resources for present and future generations and, more importantly, to act upon these problems and work to provide solutions. Collaborators focused on sustainable development will effectively educate students and generate a transition to sustainable social patterns through education and research, sustainable operation, engagement, and reporting [70]. Due to the lack of economic availability, electric vehicles were not considered in these mitigation scenarios. In 2021, only one out of 1140 automobile buyers in Mexico opted for an electric vehicle (EV). EV sales accounted for over 4% of total vehicle sales in North America. Therefore, new car insertion remains lagging in Latin America. The lack of adoption of electric vehicles in Mexico is due primarily to high prices and limited availability [71]. The majority of electric vehicles are priced above $25,000 USD, making them affordable only for the upper middle class. As a result, the transition from internal combustion vehicles to EVs will proceed at a significantly slower pace [72]. The mitigation scenarios mainly focus on cutting emissions from the electricity generation sector by 31% by 2030. The Mexican climate program does not foresee such a drastic drop in the transportation sector [73]. Although adopting electric vehicles would decrease GHG emissions in Mexico, it would also require a stronger commitment to generating energy from renewable sources. Future research may include the evaluation of mitigation measures that encourage electric vehicles. The above is in line with the Electric Mobility Strategy, which seeks to reduce CO₂ emissions by 18% in the transport sector, conforming the national fleet with electric cars in 5% by 2030 and 100% by 2050 [73].

5. Conclusions

The academic and research sectors play a key role in the sustainable implementation of CC mitigation strategies that provide both environmental and economic benefits. In the BY, the CRGS had an estimated CF of 2089 t-CO₂e, with the three main contributors of commuting, energy purchase, and business travel representing 98.2% of the total GHG emissions. The results show that CF environmental performance per unit was 0.05 t-CO2e/m², lower than the universities in the Latin American region, and CF per capita was 1.17 t-CO2e/person, similar to the national average.

The CF analysis allowed us to identify mitigation objectives. Three mitigation scenarios showed that the CF of the CRGS could be reduced by 63.5% with the implementation of strategies to minimize GHG emissions. The strategies focused on the operation of the building and the behavioral preferences of the users, for instance, using renewable energies such as solar energy, promoting collective or shared commuting, and offsetting academic trips. Furthermore, a policy of reducing attendance and offering courses in hybrid and online modalities is beneficial.

This study aims to increase awareness in societal sectors and promotes the importance of a culture of sustainability in the academic sector. This exercise highlights the need for indicators of environmental sustainability such as the efficient use of energy, water, and materials, as well as indicators of waste generation, environmental awareness, and willingness to change habits. These measures increase compliance with internal environmental policies and compliance with environmental treaties and certifications such as the Global Compact, Race Net Zero, AASHE Stars, and LEED certification.

The proposed strategies call for a comprehensive system between private and work/academic life, resulting in a shared responsibility among all participating actors. Moreover, it allows the CRGS to be an example to society and industry in efforts to combat environmental problems such as CC.