Promoting Carbon Reduction in Universities Through Carbon Footprint Assessments: A Framework and Case Study of a University in Northeast China

Abstract

To respond to the challenge of global climate change, universities should engage in carbon footprint research to identify effective strategies for mitigating greenhouse gas emissions. In this research, a comprehensive framework tailored for the study of carbon footprints in universities was constructed and used in a university in Northeast China for a case study, based on the GHG Protocol and emission factor methodology. The sources of GHG emissions at this university were identified by the GHG Protocol. Activity data were collected through questionnaire surveys and field visits. The results show that the university’s annual carbon footprint in 2022 stands at 172,473.77 t CO₂-eq, with the contributions of Scope 1, 2, and 3 accounting for 2.35%, 64.69%, and 32.96%, respectively. Based on the carbon footprint quantification results, campus carbon reduction strategies were put forward from four perspectives: individual activities, building energy management, energy-loss reduction, and carbon sink, in order to enhance the sustainability of this university. An important difference between this work and previous studies is the explicit emphasis on the necessity of the indicative role of the carbon footprint in carbon reduction efforts. The case demonstrates the application of research framework and methods, providing methodologies and case references for future research on the carbon footprint of universities.

1. Introduction

Since the advent of the industrial era, global warming, precipitated by the substantial emission of greenhouse gases (GHG) such as CO₂, has profoundly influenced human production and daily life. The atmospheric CO₂ concentration has exceeded 420 ppm, marking a 50% increase from pre-industrial levels [1]. Consequently, mitigating GHG emissions has emerged as a pivotal strategy in addressing climate change and ensuring sustainable development [2].

Environmental assessment is the basis for reducing GHG emissions. A footprint serves as a quantitative metric to assess the influence of human activities on the natural environment [3]. Notably, the carbon footprint is a crucial index for gauging the impact of these activities on climate and sustainability [4]. The carbon footprint quantifies GHG emissions from individuals, organizations, products, and activities. These emissions are then converted into CO₂ equivalents (CO₂-eq) to gauge their environmental repercussions [5]. For organizations such as universities, the carbon footprint represents the GHG emissions generated by GHG inventory associated with their operations.

Research on the carbon footprint within universities carries significant implications for sustainable societal development [6,7]. Universities, as unique and influential entities in society, play a pivotal role in spearheading and fostering responses to climate change, thereby enhancing social sustainability [8]. Furthermore, university campuses encapsulate various facets of social life, including clothing, food, housing, and transportation, thus serving as a microcosm of society. Utilizing universities as a model on a smaller scale offers direct reference value for research on comprehensive societal sustainable development.

Given the diversity among universities, there remains no unified standardized method for calculating GHG emissions from these institutions [9]. Case studies have emerged as a crucial tool in determining research methodologies related to the university carbon footprint [10]. Research on the university carbon footprint in developed nations commenced early, and currently, over 1000 universities have set zero-carbon goals, with a significant number of these institutions engaged in research on low-carbon campuses [11]. WRI/WBCSD GHG Protocol Corporate Standard (GHG Protocol) [12] is presently the most widely used standard for calculating the university carbon footprint [13]. It is internationally recognized as a benchmark for GHG accounting and is endorsed by the American College and University Presidents’ Climate Commitment (ACUPCC) [14]. This protocol encompasses three categories of GHG emissions: direct emissions owned by universities (Scope1), indirect emissions resulting from purchased energy (Scope2), and other indirect emissions (Scope3).

The specific calculation methods for carbon footprint include life cycle assessment (LCA), input-output analysis (IOA), and emission factor method [10]. The LCA method accounts for GHG emissions across all life cycle stages without a defined boundary, making it suitable for calculating the carbon footprint of specific products [15]. However, it is inapplicable to universities integrating multiple activities. The IOA method, based on economic input–output relationships, is designed for national or organizational GHG emission accounting [16]. As educational institutions, universities do not have economic activities as the dominant part of campus operations, rendering the IOA method unsuitable for most universities. Moreover, IOA lacks precision in quantifying GHG emissions from specific campus activities, limiting its utility for targeted emission reduction strategies. The emission factor method constructs emission factors for activity data corresponding to different sources, using the product of activity data and factors as estimated GHG emissions [17]. It suits diverse carbon footprint accounting needs, requires relatively minimal data collection, and can more accurately represent emissions from specific campus activities. Thus, it is optimal for university carbon footprint accounting.

Currently, China is the world’s leading consumer of energy and emitter of GHG emissions [18]. In 2017, the education sector in China accounted for 2.5% of the country’s total GHG emissions, amounting to 246 million tons [19]. As of 2020, Chinese universities consumed approximately 8.5% of the national total living consumption energy annually, with the per capita energy consumption of university students significantly exceeding the national average [20]. In 2022, there were 3013 universities in China, enrolling a total of 46.55 million students [21]. The substantial GHG emissions and energy consumption associated with university campuses and students present significant potential for emission reduction. Given this context, research into the carbon footprint of Chinese universities has gained significant importance for China’s pursuit of carbon neutrality and global sustainable development. However, research on university carbon footprints in China commenced relatively recently, and there exist common issues, such as a lack of standardization in research methodologies and challenges in data collection. For instance, Li et al.’s calculation of Tsinghua University’s carbon footprint deviates from international standards and methods, leading to the omission of many important emissions sources like heating during winter [22].

To address these concerns, this paper introduces a research framework for assessing university carbon footprints and provides a case study of a university in Northeast China based on the GHG Protocol and emission factor methodology. The objective is to offer methodological guidance and case reference that can inform future research on university carbon footprints in China and beyond, thereby contributing to the achievement of global carbon neutrality and sustainable development.

2. Methodology

2.1. Research Framework

Considering the GHG emission characteristics of university campuses and their personnel, the three types of GHG Protocol accounting scopes of universities were determined, as shown in

Table 1. Scope of GHG emissions accounting for universities based on the GHG Protocol.

Scope Type	Definition
Scope 1	Sources owned or controlled by universities, such as fossil fuel combustion and campus vehicle fleets.
Scope 2	Purchased electricity, steam, heating or cooling generated in other facilities but consumed within the university boundaries
Scope 3	Other emissions sources related to university activities but not controlled by them, such as waste treatment, water supply, faculty and student commuting.

[12,23].

Given the unique periodicity of on-campus activities, a year is selected as the accounting period for the calculation of carbon footprint. The calculation methodology employed is the emission factor methodology. The calculation equations for total carbon footprint (CF) and per capita carbon footprint (CF_per) are as follows:

$$CF=\Sigma {CF}_i=\Sigma \left(Q_i\times {EF}_i\right)$$

(1)

$${CF}_{per}=CF/N$$

(2)

In Equations (1) and (2), CF_i represents the carbon footprint of the ith emission item, Q_i and EF_i represent the corresponding activity data and GHG emission factor, respectively, for the ith emission item, and N represents the total number of students and faculty.

In this study, the research framework for the university carbon footprint is developed, as shown in Figure 1. Distinct from prior research, the GHG emission sources, as delineated by the GHG Protocol, were reclassified into individual activities and campus energy consumption to standardize and streamline data collection. Individual activities refer to the carbon footprint resulting from individual consumption behaviors related to the campus, but are not controlled and recorded by the university. Campus energy consumption refers to the direct or indirect energy consumption recorded by universities within the campus. Individual activities emissions are intricately linked to personal lifestyle habits and lack systematic management and recording, making questionnaire-based surveys an optimal method for data collection. In contrast, the consumption of various types of energy on campus is uniformly managed and documented by universities, which can be obtained from university energy management departments through surveys.

Accordingly, in Equation (1), the calculation methods for the activity data (Q_i) of individual activities and campus energy consumption are given by Equations (3) and (4), respectively.

$$Q_i=Q_{sample}\times N$$

(3)

$$Q_i=Q_{survey}$$

(4)

In the equations, Q_sample represents the average carbon footprint of the item in the sample, and Q_survey represents the total consumption of a certain type of energy.

Furthermore, the analysis and utilization of carbon footprint findings are regarded as integral parts of carbon footprint research within this framework. The objective of carbon footprint research extends beyond merely evaluating the GHG emission levels of universities. It aims to guide the reduction of GHG emissions and devise strategies for creating a carbon-neutral campus, which may necessitate further exploration and research at the corresponding stage.

2.2. Uncertainty and Countermeasures

Uncertainty represents factors that affect the accuracy of research reports. In the context of this study, which utilizes the accounting framework and Equation (1) of the GHG Protocol, uncertainties primarily stem from emission factors and activity data [24]. The standard method for analyzing emission factors is LCA. However, measuring GHG emission factors for specific universities presents a challenge. Consequently, the values of emission factors must be sourced from regional LCA literature reports and databases where the university is situated, leading to potential deviations from the actual value. In the computation process, it is advisable to select the emission factor corresponding to the region with the narrowest available range and most recent data, and the sources of emission factors should be as consistent as possible. In contrast, the uncertainty associated with activity data is more extensive and holds a more significant direct impact on the accuracy of carbon footprint calculation results. To mitigate this, emission items should be meticulously categorized before data collection to prevent duplicate records of activity data. Additionally, activity data gathered through questionnaire surveys must be carefully organized and screened to ensure their reliability.

3. Data Acquisition

3.1. Identification of Inventory and Emission Factors

The targeted university is situated in the northeastern region of China, encompassing approximately 40,000 students and faculty in 2022. The academic year for students typically spans an approximate duration of 40 weeks.

The determination of specific emission items for each of these scopes was approached with the consideration of both research feasibility and the magnitude of GHG emissions. The GHG emission factors utilized in this study were from the “China Products Carbon Footprint Factors Database (2022)” [25]. The specific values of these emission factors were determined following the principles outlined in the framework. For the target university, the identified emission sources and selected emission factors are presented in

Table 2. GHG inventory of the university.

Scope of GHG Protocol	Emission Item	Emission Factor	Unit	Activity Type
Scope 1	Natural gas	2.80	kg CO2-eq/m3	Campus energy consumption
	Gasoline	3.85	t CO2-eq/t
	Diesel oil	3.82	t CO2-eq/t
Scope 2	Electricity	0.88	kg CO2-eq/KWH
	Heating	0.15	kg CO2-eq/KWH
Scope 3	Clothing (shoes)	7.47	kg CO2-eq/dual	Individual activities
	Clothing (jeans)	16.42	kg CO2-eq/pair
	Clothing (down jacket)	3.34	kg CO2-eq/piece
	Clothing (chemical fiber clothing)	25.70	kg CO2-eq/kg
	Clothing (cotton clothing)	18.12	kg CO2-eq/kg
	Potable water	1.13	kg CO2-eq/m3
	Egg	3.58	kg CO2-eq/kg
	Vegetables and fruits	0.521	kg CO2-eq/kg
	Staple food (pasta)	1.33	kg CO2-eq/kg
	Staple food (rice)	1.37	kg CO2-eq/kg
	Meat	12.88	kg CO2-eq/kg
	Student commuting (by car)	0.041	kg CO2-eq/(person·km)
	Student commuting (by plane)	0.088	kg CO2-eq/(person·km)
	Student commuting (by high-speed railway)	0.026	kg CO2-eq/(person·km)
	Student commuting (by train)	0.01226	kg CO2-eq/(person·km)
	Consumables (express packing boxes)	1.14	kg CO2-eq/kg
	Consumables (disposable chopsticks)	4.60	kg CO2-eq/kg
	Consumables (A4 paper)	1.76	kg CO2-eq/kg
	Consumables (plastic bags)	8.21	kg CO2-eq/kg

.

3.2. Activity Data Collection

The emission activity data pertaining to individual activities were procured via online questionnaire surveys, and the content of the questionnaire was segmented into three distinct sections. The initial section necessitates the provision of essential personal information. The second section pertains to the requisite transportation associated with the campus, encompassing the preferred method and the geographical distance for student commuting. The final section focuses on other personal daily activities. The items in question stem align with Table 2, with the time period being adjusted according to the frequency cycle of activities and ultimately converted into a one-year period for calculation purposes.

The questionnaire survey was administered from December 2022 to February 2023 via an online platform for distribution and collection. Considering different academic years and gender ratios, the questionnaires were randomly distributed approximately in proportion to the ratios to cover different subgroups while ensuring randomness. Out of the 254 distributed questionnaires, 250 were collected. Following a rigorous screening process, 213 valid questionnaires were identified, resulting in a valid response rate of 83.9%. The gender distribution within the valid questionnaires approximated a 4:1 ratio, aligning with the university’s actual gender demographics. This ensures a relatively unbiased representation of the carbon footprint associated with individual activities. The sampled activity data was meticulously screened and organized using the SPSSAU platform, as shown in

Table 3. Activity data of collected samples.

Emission Item	Unit	Minimum	Maximum	Mean Value	Standard Deviation	Median
Cotton clothing	pieces/year	3	13	4.939	3.000	3
Chemical fiber clothing	pieces/year	3	13	4.225	2.573	3
Down jacket	pieces/year	0	6	0.704	1.069	0
Jeans	pair/year	0	3	0.962	0.999	1
shoes	pair/year	1	9	2.667	2.048	1
Plastic bag	PCS/week	5	31	6.718	4.260	5
A4 paper	sheet/week	5	31	9.122	6.223	5
Disposable chopsticks	pairs/week	4	18	4.756	2.476	4
Express	PCS/Week	2	10	2.563	1.483	2
Meat	g/day	0	300	174.178	69.635	200
Rice	g/day	50	300	209.859	83.533	200
Pasta	g/day	100	500	181.221	83.691	200
Fruit and vegetable	g/day	150	750	392.488	191.850	300
Egg	PCS/day	0	4	1.737	0.914	2
Drinking water	mL/day	500	3500	1556.338	718.108	1500

, and the mean value has been used for calculating the carbon footprint.

On campus energy consumption, the research team undertook field investigations, collecting activity data related to electricity, heating, natural gas, diesel, and gasoline. This was achieved by visiting the university’s energy management and logistics departments, inspecting primary energy consumption sites on campus, and analyzing various types of energy consumption. For the convenience of further analysis, the research team obtained activity data spanning the period from 2018 to 2022.

It should be noted that, as a boarding university, the daily commuting of students in this university is conducted via on-campus buses operated independently by the campus. The carbon footprint thereof stems from energy consumption such as fuel and electricity, which is included in energy consumption activity data. Thus, “student commuting” in this paper refers exclusively to the round trips between their hometowns and the university during winter and summer vacations (4 times a year), which differs from the definitions in other articles.

4. Result and Discussion

Section 4.1 elucidates the computational outcomes and distribution of the carbon footprint for the specified university, comparing it with other universities. Section 4.2 consequently analyzes GHG emissions and proposes relevant mitigation strategies based on the quantitative results of the carbon footprint.

4.1. Carbon Footprint Calculation Results

According to Equation (1), the annual carbon footprint of the targeted university is 172,473.77 t CO₂-eq, with the carbon footprint for Scope 1, 2, and 3 being 4058.21 t CO₂-eq, 111,570.40 t CO₂-eq, and 56,845.16 t CO₂-eq, respectively, accounting for 2.35%, 64.69%, and 32.96% of the total carbon footprint, as detailed in

Table 4. Carbon footprint calculation results of the university.

Scope of GHG Protocol	Emission Item	Carbon Footprint (t CO2-eq.)
Scope 1	Natural gas	3777.48
	Gasoline	60.29
	Diesel oil	220.44
Scope 2	Electricity	74,765.06
	Heating	36,805.34
Scope 3	Clothing	5345.92
	Potable water	19.70
	Egg	2753.07
	Vegetables and fruits	2290.25
	Staple food	5919.54
	Meat	23,604.66
	Student commuting	16,113.53
	Consumables	798.49

. The per capita carbon footprint is 4311.84 kg CO₂-eq.

The primary source of the carbon footprint for Scope 1 is natural gas usage, while for Scope 3, it primarily stems from meat in daily meals and student commuting. Indirect emissions are the main source of the university’s carbon footprint. This is because universities are not industrial sites, and the primary sources of their carbon footprint derive from external energy inputs and the relevant consumption activities of on-campus personnel, with the utilization of fossil fuel supplies limited to a few instances, such as natural gas for canteens and swimming pools. This finding is also consistent with results from some previous studies [26,27].

This study summarizes the carbon footprints of 12 universities from diverse countries and regions, as presented in

Table 5. Carbon footprint accounting results of 12 universities.

University	Country	Research Year(s)	CF per Capita (t CO2-eq.)	Source
University of Cape Town	South Africa	2007	4.00	[28]
The Norwegian University of Technology and Science	Norway	2009	4.60	[29]
Tongji University	China	2009–2010	3.84	[30]
University of Leeds,	England	2010–2011	4.27	[31]
Clemson University	America	2014	4.40	[32]
TERI University	India	2014–2015	0.72	[17]
University of Talca	Chile	2016	0.72	[33]
Autonomous Metropolitan University. Cuajimalpa Campus	Mexico	2016	1.07	[34]
Technological University of Pereira	Colombia	2017	0.40	[35]
University of Oulu	Finland	2019	1.13	[10]
University of Bologna	Italy	2018/2020	0.58/0.18	[36]
The targeted university	China	2022	4.31	This research

. Considering the scale differences among different universities, per capita carbon footprint is adopted here. Evidently, the per capita carbon footprint of institutions in the early stage was around 4.0 tCO₂-eq. In recent years, there has been a substantial overall decline in the carbon footprint of universities. This decline can be ascribed to the inherent variations in emission sources among these universities. For instance, low-latitude universities like TERI University do not need heating during winter. However, the more crucial factor is the early implementation of sustainable campus construction initiatives in these universities, which have achieved remarkable results, especially among European universities.

The carbon footprint of the target university remains at the level of early-stage research, indicating that its carbon reduction endeavors are still in their infancy. To address this, it is imperative to implement targeted measures to cut down on GHG emissions, and such efforts should be guided by the quantified results of the carbon footprint.

4.2. Carbon Reduction Strategies Analysis

Considering the inherent disparities in the emission characteristics of individual activities and campus energy consumption, this section will undertake distinct analyses of these two components to seek targeted measures for reducing GHG emissions. Moreover, an assessment of the campus green space carbon sink will be performed. The carbon footprint amounted to 115,628.61 t CO₂-eq for campus energy consumption and 56,845.16 t CO₂-eq for individual activities, respectively. The specific composition and distribution of the carbon footprint are depicted in Figure 2.

4.2.1. Individual Activities

As shown in Figure 3, the per capita carbon footprint distribution for the total sample, as well as for both male and female participants, approximates a normal distribution, indicating that the data collected is logical. While males exhibited a slightly larger carbon footprint than females, the variances among the three groups were minimal (within 5%), and thus they were analyzed as a totality.

Over half of the carbon footprint related to individual activities stems from dietary choices. This is attributable, on the one hand, to high overall consumption levels, and on the other, to the high-carbon nature of the dietary structure in which components with high-emission factors, such as meat, account for a relatively large proportion. Food emission factors predominantly arise from GHG emissions at the point of food production, and it is beyond the control of the university and challenging to mitigate in the short term. The university must promote a more vegetarian and sustainable dietary pattern among student populations.

Another major source of carbon footprint related to individual activities is student commuting, as detailed in

Table 6. Specific composition of the carbon footprint from student commuting.

Type of Transportation Modes	Selection Rate	Annual Average Distance (km)	Annual Carbon Footprint (t CO2-eq)
By car	14.09%	152.15	151.05
By plane	43.66%	2412.16	14,329.39
By high-speed railway	8.92%	1151.39	580.80
By train	33.33%	1461.85	1052.29

. It can be seen that aircraft emissions constitute the majority. Universities and governments should promote more student-friendly policies regarding train tickets to encourage students to opt for a lower-carbon transportation mode in long-distance commuting. While other consumption contributes only about 11% to the carbon footprint of individual activities, this suggests that students at this university adopt a relatively low-carbon lifestyle in terms of resource utilization.

4.2.2. Building Energy Management

Campus energy consumption constitutes two-thirds of the total carbon footprint, indicating an increased demand for research into carbon reduction strategies. As shown in Figure 4, over the preceding five years, the carbon footprint associated with campus energy consumption at this university has exhibited a pattern of initial decline followed by an increase. The lowest value was recorded in 2020, while the peak was observed in 2022. The notable escalation post-2020 is likely attributable to the expansion of energy-intensive facilities on campus and a marked rise in indoor activities duration for faculty and students.

To assess the extent of waste in campus energy consumption, this study has defined parameters to evaluate the energy use efficiency of building energy consumption as follows:

$${EUE}_i=N_{rt}/N_d$$

(5)

In Equation (5), EUE_i represents the real-time energy use efficiency of a specific building, N_rt represents the real-time number of individuals inside the building, and N_d represents the designed capacity of the building.

Equation (5) rests on the interpretation and application of the concept of theoretical occupancy. The theoretical occupancy capacity refers to the number of people set by architects and energy engineers during the design of buildings, with full consideration of individual energy demands. It represents the number of people that a building can normally accommodate under standard load conditions, thus it can be regarded as the standard for the building to avoid energy waste.

The energy intensity of teaching, accommodation, and public spaces is notably high [37]. This study employs 22 student apartments, three teaching buildings, and a natatorium in the university as representative samples to assess the energy use efficiency of dormitories, educational facilities, and public sports facilities, subsequently evaluating the necessity of energy management in campus buildings.

The designed capacity of each building was determined using publicly available information about the campus buildings. The flow of individuals within each building was calculated based on surveys and data analysis. For teaching buildings, the flow data was derived from a statistical conversion of the number of students attending classes and those studying independently. The flow in dormitories was calculated using the number of residents and facial recognition records at entrance and exit gates, with a baseline established at 4 a.m. The flow in the natatorium was obtained from the records by the venue management department over the past year. Upon calculation, it was found that the average energy use efficiency for teaching buildings, dormitories, and natatoriums throughout one day was 9.9%, 63.4%, and 32.7%, respectively, as shown in Figure 5.

Based on a further survey and the average energy use efficiency of various campus buildings, it is estimated that within a day, the natatorium unnecessarily wastes 383 m³ of natural gas and 24,078 MJ of heating energy. The teaching building unnecessarily wastes 134,197 MJ of heating energy, while the dormitory building unnecessarily wastes 2633 KWH of electricity and 159,000 MJ of heating energy. From this observation, the energy input significantly surpasses actual requirements, indicating a substantial potential for carbon reduction in energy management practices. It is imperative to modulate the energy supply in alignment with actual requirements. During the winter vacation, it is feasible to decrease the heating supply in certain regions. According to an investigation, this approach can yield annual savings of nearly one million yuan in heating expenses. The suboptimal utilization of public spaces can be attributed to the demand for private space. For example, a classroom is generally used by only 4–5 students for self-directed learning or group discussions. Therefore, it recommended incorporate small cubicles to mitigate this issue.

Further energy management necessitates the development of a comprehensive monitoring and maintenance system. The investigation found a lack of hardware to monitor energy usage across various buildings. To address this, the university should incorporate energy metering and control devices into campus buildings. By constructing a campus “energy network”, the efficiency and management level of building operations can be significantly improved. This enables a rational adjustment of energy supply on the supply side, leading to a decrease in energy consumption and, consequently, a reduction in GHG emissions.

4.2.3. Energy Loss Reduction

Beyond merely adjusting the energy supply through monitoring and management measures, it is also crucial to reduce energy dissipation on the consumption side. Energy is mainly dissipated in the form of heat within the university, necessitating technological measures to reduce this heat loss in order to decrease GHG emissions. On one hand, it is imperative to adopt passive energy-saving designs. The university, situated in northeastern China, experiences high heating demands. However, the thermal lag effect precludes immediate on-off switching of heating systems. To mitigate this issue, interconnecting corridors between buildings can be constructed to minimize heat loss from people entering and leaving the building door. For individual buildings, passive energy-saving design strategies can be implemented. These include designing optimal window-to-wall ratios, orientations, insulation layer positions and thicknesses, and door and window structures to enhance the building’s insulation and lighting effects, thereby achieving the goal of reducing unit energy consumption. On the other hand, waste heat recovery technology should be applied to high-grade thermal energy, utilizing various types of heat pumps for secondary supply to realize the concept of “waste heat not wasted.” For example, during winter, hot water from bathing activities can be harvested and channeled to supply heat to other areas via a wastewater source heat pump. Conversely, in summer, a ground source heat pump can be employed to gather the heat produced by cooling processes while offering supplementary heating for the natatorium and bathrooms.

4.2.4. Carbon Sink

Carbon sinks are measures with persistent benefits that must be considered for achieving carbon neutrality within a region [38], playing a crucial role in achieving carbon neutrality on campus. This is primarily achieved by the expansion of green spaces on campus, and harnessing plants’ carbon sequestration capabilities to enhance carbon absorption. Current data indicates that the university’s existing and in-progress green spaces span 523,617 m², which absorb 1353.37 tons of carbon dioxide annually, equivalent to 0.78% of the carbon footprint in 2022, as detailed in

Table 7. Estimation of carbon sink in the campus green space.

Type of Green Space	Area (m2)	Absorption Coefficient (kg/m2) [39]	Annual Carbon Sink (kg CO2-eq)
lawn	35,336	2.01	71,025.36
shrub	235,498	2.59	609,939.82
arbor	252,783	2.66	672,402.78

.

5. Novelty and Limitations

This section summarizes the most innovative and generalizable ideas and practices proposed in this paper regarding the carbon footprint accounting method, carbon reduction analysis methodology, and case study, as well as their corresponding limitations.

5.1. Development of the Accounting Method

In existing GHG Protocol, activity data for Scope 3 emission sources such as food procurement should all be derived from the unified records of the organization [12]. However, most universities currently struggle to achieve this. Responding to the real-life conditions of universities, this paper proposes that questionnaire surveys can be adopted to collect activity data for emission sources lacking unified records in Scope 3. This approach provides a feasible means for quantifying these emissions and can benefit numerous universities and other organizations with similar operational conditions.

This refinement takes into account the objective limitations of organizations such as universities, substantially enhancing the universality of both the accounting inventory and the methodology. However, enhanced universality often entails diminished precision. Thus, organizations capable of conducting accounting fully in accordance with the GHG Protocol should do so to enhance the accuracy of accounting results.

5.2. Methodology for Carbon Reduction Analysis

Previous studies have exhibited two tendencies. One is to discuss each emission item in the inventory in detail; however, most universities lack sufficient data to support such a comprehensive analysis, and discussions lacking adequate empirical grounding risk being superficial. For example, in the study by Mendoza-Flores et al., each emission item of the Cuajimalpa campus of the Autonomous Metropolitan University was discussed, but few practical carbon reduction measures were proposed [34].

Another common approach is to develop campus carbon neutrality models based on existing national policies, technical means, and assumptions. For instance, Wang et al. established a 2060 carbon neutrality model for a medium-sized university campus in eastern China by integrating seven aspects, including power-sector decarbonization trends and rooftop photovoltaics [19]. The limitation of this approach is that it can only provide idealized solutions and, to some extent, overlooks the role of the carbon footprint as an environmental indicator, as well as the university’s own efforts.

This paper adopts the approach of conducting targeted discussions on major GHG emission sources, instead of exhaustively analyzing every emission source in the inventory or constructing idealized scenarios. By jointly considering the magnitude of emissions and the university’s controllability over each emission source, the study concentrates on those with large emissions and the greatest mitigation potential, while omitting discussion of sources that are either minor contributors or unlikely to yield significant near-term reductions through university-level interventions.

This approach optimally utilizes the carbon footprint as an indicator for assessing carbon reduction, enhancing the feasibility of further analysis while ensuring the proposal of valuable carbon reduction strategies. However, theoretically, the deliberate omission of minor factors unavoidably reduces the accuracy and comprehensiveness of the discussion. Therefore, researchers must explicitly define the scope of negligible factors in line with the research objectives.

5.3. Proposal of the Energy Use Efficiency Indicator

The energy use efficiency proposed in the case study of this paper holds significant practical value and broad applicability.

The energy supply for campus facilities in the vast majority of universities operates under the same “0–1” mode as the university examined here: during operating hours, facilities remain at full load regardless of occupancy to guarantee that any potential user’s energy demand is met. In this case, managerial waste, rather than modest equipment-level savings, constitutes the main cause of energy waste. Energy use efficiency therefore serves as a straightforward metric for administrators to adjust the number of open facilities. For example, if the energy use efficiency of a certain venue during a specific period remains below 30%, the energy supply to up to 70% of the facilities can be shut down, yielding an immediate 70% reduction in both energy consumption and GHG emissions. This metric is not only applicable to university campuses, but can also be extended to other organizations.

In practice, due to various special needs, facilities and venues cannot be opened or closed solely based on energy occupancy rate. However, this metric provides administrators with a valuable upper-bound reference for energy savings achievable through management.

6. Conclusions

The carbon footprint has emerged as a foundational tool for organizations aiming to curtail greenhouse gas emissions and enhance sustainability. While general guidelines for carbon footprint calculation exist, they usually do not fully consider the specificity of organizations such as higher education institutions.

This paper develops a research framework for carbon footprint studies within universities and provides a case study of a university in Northeast China to extend available methodologies tested under real-life conditions, based on the GHG Protocol and emission factor methodology. Compared with previous studies, this study has refined and enhanced the methodology in several areas:

(1)

It introduced an investigation and analysis methodology from the perspectives of individual activities and campus energy consumption, streamlining and standardizing the research process for university contexts;

(2)

It refined and developed a quantitative analysis method centered on the carbon footprint, highlighting its indicative role in carbon reduction efforts;

(3)

It defined and computed building energy use efficiency metrics, revealing prevalent energy wastage in universities and emphasizing the imperative of energy management systems.

The results show that the annual carbon footprint of this university in 2022 is 172,473.77 t CO₂-eq, with a carbon footprint generated by Scope 1, 2, and 3 constituting 2.35%, 64.69%, and 32.96% of the total carbon footprint, respectively. Electricity consumption was the largest contributor to GHG emissions (43.35%), followed by heating (21.34%) and dietary emissions (20.05%). Based on the quantitative carbon footprint results by activity type, targeted measures were proposed to diminish GHG emissions and improve sustainability, encompassing dietary structure modification, energy consumption monitoring management, passive energy-saving design, waste heat utilization, and the enhancement of carbon sinks. In particular, building energy management is a paramount way for carbon reduction within the university. This approach not only underpins carbon footprint research, but also highlights its practical implications in guiding sustainable development.

It must be acknowledged that, due to constraints in the survey duration and feasibility, certain limitations exist in the completeness of the accounting boundary and the adequacy of the sample size. Additionally, simplifications made in the analytical approach to enhance generalizability may slightly reduce the accuracy and comprehensiveness of the discussion. Despite these limitations, this paper offers a practical case for future research reference. Moreover, the research framework proposed herein can contribute to refining research methodologies and enhancing the research system for university carbon footprint studies. On the basis of this paper, future research needs to consider how to enhance the scientificity of research design and the comprehensiveness and accuracy of analysis.