THAI NGUYEN UNIVERSITY UNIVERSITY OF AGRICULTURE AND FORESTRY ANGELICA MILETTE SUNICO ADRIANO CARBON FOOTPRINT ESTIMATION OF TRANSPORTATION OF THAI NGUYEN UNIVERSITY OF AGRICULTURE AND
INTRODUCTION
Research Rationale
Carbon dioxide is crucial for regulating Earth's temperature by absorbing and radiating heat However, excessive human-induced emissions have led to it being responsible for two-thirds of the planet's energy imbalance Anthropogenic greenhouse gas emissions are the primary driver of global climate change, prompting urgent action from countries and international organizations (IPCC, 2006) Since the 1980s, rapid population growth, industrialization, urbanization, and technological advancements have exacerbated environmental challenges worldwide (Borucke et al., 2011).
Vietnam ranks 30th globally in energy production and CO2 emissions, with a per capita emission value of 1.82 metric tons as of 2014 (World Bank, 2016) The country demonstrates its commitment to addressing climate change through its Intended Nationally Determined Contribution (INDC), which outlines various national policies and specific actions for mitigating greenhouse gas emissions The INDC encompasses the entire economy, including the transport sector, which contributed over 10.8% of Vietnam's CO2 emissions in 2019 This sector's emissions are projected to increase by 6-7% annually, potentially reaching nearly 70 million tons of CO2 (Anh, 2019).
Universities, as educational, research, and community service organizations, play a vital role in promoting sustainability and should serve as a model for other
Thai Nguyen University of Agriculture and Forestry (TUAF), established in 1969, has evolved over 50 years into a trusted member of Thai Nguyen University, with a commitment to creating an eco-friendly and green campus The university focuses on producing graduates equipped to maintain sustainable living, recognizing the significant impact of student activities on carbon emissions.
CF serves as an essential decision-making tool for organizations, particularly universities, enabling them to enhance control over their environmentally impactful activities It also offers a measurable value that facilitates comparisons of environmental impacts among various academic institutions (Robinson et al., 2018).
This study aims to estimate the carbon dioxide equivalent (CO2e) emissions of TUAF students The data collected will provide valuable insights for the Vietnamese government on strategies to reduce the carbon footprint of both local and international students residing in Vietnam.
Research Objectives
This study aims to determine these objectives:
1 To estimate the academic transportation carbon emission of TUAF students
2 To determine the most used mode of transportation of the students
3 To provide quantifiable data to the school so it can be used for future purposes.
Research Question And Hypotheses
The researcher aimed to answer these questions:
• What is the estimated carbon footprint of students living in the dormitory and in their hometown by their academic transportation habit?
1 Null hypothesis (H o ) – The CO2e from academic transportation of students living in the dormitory is the same with the CO2e from academic transportation of students living in their hometown
2 Alternative hypothesis (H a )- The CO2e from academic transportation of students living in the dormitory is different from the CO2e from academic transportation of students living in their hometown.
Scope And Limitations
The scope of the study is mainly focused on the estimated CO2e of students’ academic transportation in the 1 st semester of academic year 2020-2021
However, there are limitations considered such as;
- the lack of existing annual CO2e data in TUAF,
The distance measured reflects a one-way journey from point A to point B, indicating that the data collected pertains solely to the route taken by students from their accommodation to the university, without considering the return trip.
- only the average emission factor per vehicle is used, regardless of the brand and model of the vehicle, and;
- the flexibility of the schedule of the students was not indicated as a factor to consider
Definition Of Terms
Academic transportation is the transportation of students from the point where they are staying to the place where they study such as classrooms, buildings or school itself
Anthropogenic greenhouse gases is simply personal GHGs It is due to personal activities of humans that emit GHG and precursors
Carbon footprint is the entire amount of greenhouse gases (including carbon dioxide and methane) produced by our actions
Emission factor is a representative figure that seeks to link the amount of a pollutant emitted into the atmosphere with the activities connected with that release
LITERATURE REVIEW
Carbon Footprint
Since 1800, atmospheric carbon dioxide (CO2) levels have increased significantly from 279 to 397 parts per million, primarily due to fossil fuel consumption Recently, the concept of "carbon footprint" has gained considerable attention, particularly in media discussions.
The carbon footprint refers to the total greenhouse gases (GHGs) emitted as a result of a specific action, process, organization, or entity This concept is flexible and heavily depends on the definitions of scope and methodology used.
2015) For example, depending on the amount of gas consumed and the distance travelled, the combustion of petrol produces a specific amount of CO2 (Qafisheh et al.,
Recent studies indicate that food transportation via air routes has significantly increased carbon emissions (Konicyzni, 2013) Additionally, vehicles used in this process contribute to one of the highest product-specific carbon footprints (McIntosh & Pontius, 2017).
Transportation is essential in our daily lives, with various modes facilitating our movement to desired locations However, it is responsible for 95 percent of greenhouse gas emissions, primarily in the form of carbon dioxide This emissions are largely produced by cars, SUVs, and pickup trucks that operate on gasoline, diesel, and other fuels, which together contribute to nearly two-thirds of transportation-related emissions Consequently, transportation ranks as the second-largest source of total greenhouse gas emissions in the United States.
Figure 2.1 Transportation Accounts For 29% of U.S Greenhouse Gas
(Source: U.S Department of Transportation Federal Transit Administration,
The quantification of greenhouse gas emissions, particularly carbon emissions, is essential across eight key sectors: construction, housing, food, clothing, mobility, manufactured goods, services, and trade (Hertwich & Peters, 2009) The increasing demand for accurate carbon calculations stems from the adverse effects of significant greenhouse gas emissions on global climate conditions Various methods, including simple online calculators and comprehensive lifecycle assessments, have been developed to estimate carbon footprints (Terry, 2015) To effectively compute carbon footprints, it is crucial to relate them to ecological footprints Carbon dioxide (CO2) emissions are typically measured in tons per year (Qafisheh et al., 2017) However, the Covid-19 pandemic prompted the education sector to shift to online classes, leading researchers to hypothesize that students would not need to commute to campus.
10 this school year, the researcher used the 1 st semester of academic year 2020-2021 to estimate the students’ academic transportation carbon emission
Carbon emissions differ significantly across the globe, with industrialized countries exhibiting higher carbon footprints For instance, in the same year, France recorded a per capita carbon footprint of 6.0 metric tons, while Brazil and Tanzania had much lower footprints of 1.8 metric tons and 0.1 metric ton of CO2 equivalent, respectively (Selin, 2020).
Figure 2.2 Annual CO 2 Emissions in the World
Since the mid-eighteenth century, global emissions have significantly increased, as illustrated in Figure 2.2 Prior to the Industrial Revolution, emissions were relatively low, with growth remaining moderate until the mid-twentieth century.
In the twentieth century, global CO2 emissions reached 6 billion tonnes in 1950, and have since surged to over 34 billion tonnes annually Although emissions peaked in recent years and saw a slight decline during late 2020 and early 2021, there is an urgent need for effective measures to reduce these emissions.
Carbon Footprint Emission Calculation In Universities
Universities are increasingly aligning with the global trend of low-carbon organizations, necessitating effective emission allocation techniques to manage greenhouse gas (GHG) emissions Various strategies exist to reduce carbon emissions, and university campuses, with their centralized structures and dense populations, serve as ideal testing grounds for these measures However, a critical first step is to obtain quantifiable data on the organization's carbon dioxide equivalent (CO2e) emissions.
Researchers at a university in Shanghai developed a novel method to assess the average personal carbon footprint of students (Li et al., 2014) Despite challenges in obtaining data, Li and his team successfully conducted an online survey to gather information on students' energy consumption Their findings revealed that the annual average carbon emissions per student amounted to 3.84 tCO2e.
University carbon footprints (CFs) per capita represent, on average, 23% of the national per capita carbon footprints, with a range of 12% to 37% The European Commission states that national per capita carbon emissions encompass emissions from fossil fuel consumption, industrial activities, and product usage (Crippa et al., 2019).
Figure 2.3 University CFs per capita and year relative to national per capita footprints
(Source: Environmental Sciences Europe, Helmers et al., 2021)
The relationship between university per capita carbon footprints (CFs) and national CFs is anticipated, as emissions from electricity generation typically dominate both This trend holds true for emissions resulting from electricity consumption in universities Since universities lack their own power plants, they rely on the national electrical grid for their energy needs.
Many studies assessing the CO2e emissions of universities have concentrated on their overall design, operations, and supply chain strategies For example, Ozawa-Meida et al (2013) examined a UK university by integrating a top-down supply chain economic input-output analysis of emission components with a bottom-up life cycle assessment (LCA) approach to account for activity intensities, ultimately evaluating the institution's total carbon footprint.
Research indicates that carbon emissions are nearly evenly distributed across building energy usage, transportation, and purchases Baboulet and Lenzen (2010) analyzed the carbon footprint of an Australian institution using financial reports, while Alvarez et al (2014) assessed the carbon footprint of the Technical University of Madrid's faculty of forestry engineering, which totaled 2147 tCO2e in 2010 Larsen et al (2013) found that students in social sciences and humanities at the Norwegian University of Technology and Science had a carbon footprint approximately 50% lower than their peers in natural sciences and engineering Güereca et al (2013) reported that at the Universidad Nacional Autonoma de Mexico, energy usage contributed to 42% of total GHG emissions, with transportation accounting for 50% Additionally, a university in Thailand utilized a web-based carbon footprint calculator from the Thailand Greenhouse Gas Management Organization (TGO) to measure student carbon emissions, focusing specifically on transportation (Utaraskul, 2015).
For the researcher of this study, distance-based method is the simplest way to acquire a quantifiable data which later on can be used and developed
Road vehicle emissions significantly contribute to air pollution, particularly in urban areas (Davison et al., 2020) The European Environment Agency (2019) reports that 86 percent of monitoring stations identifying NO2 concentrations are located at traffic sites, highlighting the impact of vehicular traffic on air quality.
Quality Guidelines in 2017 Schraufnagel et al (2019) have proposed that air pollution might cause persistent harm to every organ in the human body
Reliable emissions data is crucial for the success of air pollution reduction programs Accurately quantifying road vehicle emissions presents significant challenges due to various influencing factors, such as the manufacturer, vehicle model, emission standards, engine size, and fuel type.
In recent years, there has been an increased focus on measuring emissions under "real-world" conditions, moving beyond traditional laboratory-based assessments The Real Driving Emissions (RDE) test is conducted in actual traffic scenarios, utilizing Portable Emission Measuring Systems (PEMS) to monitor vehicles across various driving environments, including urban, rural, and highway settings.
PEMS and remote sensing complement each other effectively, with PEMS providing significant advantages by monitoring an individual vehicle's entire journey across various driving conditions, from idling to highway travel However, the process of measuring a large number of vehicles can be costly and time-consuming, especially when accounting for factors like ambient conditions, vehicle age, and degradation effects Additionally, capturing data from a wide range of vehicle types, including urban buses and heavy-duty diesel vehicles (HDVs), presents challenges, as the existing PEMS databases are predominantly focused on passenger cars.
Fuel-based emission factors, most usually stated as grams of emission per kilogram of fuel burned, are simple to compute using a few fundamental assumptions
Research on hydrocarbon fuel combustion has evolved, with studies like those by Burgard et al (2006) and Tong et al (2000) utilizing roadside observations to predict fuel consumption Chan and Ning (2005) built upon Tong et al.'s findings to estimate fuel use based on instantaneous vehicle speed Zhou et al (2007) further refined these estimates by incorporating vehicle specific power (VSP) and speed, adjusting fuel consumption figures for vehicle mass to reflect real-world driving conditions This paper focuses on calculating vehicle emissions based on the distance traveled by students, using an emission factor per vehicle to quantify emissions relative to the distance covered.
METHODS
Research Locale
This study takes place at Thai Nguyen University of Agriculture and Forestry (TUAF), established in 1969 and recognized as a reputable member of Thai Nguyen University The university's campus, as illustrated in Figure 3.1, encompasses a perimeter of 2,208.74 meters and covers an area of 254,940.63 square meters.
Figure 3.1 TUAF Map (Source: Google Earth)
Over the past 50 years, Thai Nguyen University of Agriculture and Forestry (TUAF) has consistently evolved, now striving to establish itself as an eco-friendly and green campus The university is committed to innovation and creativity, positioning itself as a leading institution in agricultural science training, research, and knowledge transfer, thereby contributing significantly to the advancement of Thai Nguyen University.
Materials
The materials used to conduct data collection and analysis are as follows:
1 Questionnaire distributed to the respondents through Google Forms The researcher opted to spread the survey through an online platform due to Covid-19 restrictions
2 Calculator.carbonfootprint.com is used to compute the personal transportation carbon footprint of students who are using bus as their mode of transportation to school to carefully assess whether the computation is accurate or close to the estimation
3 Distance-based method/equation is used to compute the carbon footprint estimation of students who are using cars, motorbikes, e-bikes and walking as their mode of transportation to go to school
4 Laptop is used for writing and searching related studies and researches
5 Notebook and scientific calculator is used to compute the two-tailed t-test, which will test the hypothesis
6 Microsoft Excel is used to solve the descriptive statistics To ensure accuracy, the researcher opted to use Excel.
Research Design
This research posits that there is a significant difference in the CO2e emissions from transportation for students living in dormitories compared to those from their hometowns, employing a quantitative design Descriptive research is an appropriate choice for this study as it aims to uncover traits, frequencies, trends, and classifications (McCombes, 2020).
Sampling Technique
The researcher used a non-probability sampling method called quota sampling Ideally the quota of the researcher is to ask 50 students living in the dormitory and 50
18 students living in their hometown/own houses to answer the survey All of the questions are close-ended to indicate the characteristics of the respondents
The respondents are assumed to be composed of local/foreign students which led the researcher to provide the questionnaire both in English and in Vietnamese.
Data Analysis
Following the survey, the researcher implemented several steps to determine the personal carbon footprint of the respondents Participants were provided with estimated distances from their residence to their educational institution.
3.5.1 TRANSPORTATION CARBON FOOTPRINT DATA ANALYSIS
The personal transportation carbon footprint data is acquired through a survey conducted with 50 students that live in the dormitory and 50 students that live in their hometown or their own houses
The estimated CO2 emissions will be calculated using a distance-based methodology, which involves summing emissions for each mode of transportation This is achieved by multiplying the activity level, represented by the distance traveled by vehicles, with an emission factor measured in kilograms tonnes/km for each mode Gathering accurate vehicle distance data is crucial, as it directly influences emissions calculations The total emissions can be determined using the provided equation, which accounts for all vehicle types.
Air pollutant emission factors (EFs) from indoor solid fuel combustion are influenced by various testing techniques, fuel-stove systems, sampling and analysis tools, and climatic conditions Recent research highlights significant advancements in understanding pollutant EFs, particularly the contrasts between laboratory and field studies, the quantification of fugitive emissions, and the factors contributing to variability in EFs (Shen et al., 2021).
According to Eggleston (2006) road transport mostly emits CO2, NO2, CO, and NMVOCs, but it also produces a tiny amount of N2O, CH4, and NH3 As a result,
CO2 is the primary greenhouse gas emitted directly, prompting researchers to utilize emission factors for various transportation modes, primarily based on CO2, as detailed in Table 3.1.
An emission factor is a representative figure that seeks to link the amount of a pollutant emitted into the atmosphere with the activity connected with that release (Cherimisinoff & Rosenfeld, 2011)
Table 3.1 List of Emission Factor for Each Mode of Transportation in tCO 2 e/km
Emission Factor of Each Mode of Transportation
(Sources: European Cyclists Federation (2020), Thrust Carbon (2021), Statista
The emission factors will be used on the distance-based equation to give value for the carbon emission of students’ transportation
This study tests the hypothesis that students residing in dormitories have a lower academic transportation carbon footprint compared to those living in their own homes To evaluate this, a t-test was employed to compare the means of the two groups, determining if a significant difference exists between them According to Bevans (2020), the t-test is commonly utilized in hypothesis testing to assess the impact of a method or treatment on a specific population or to identify variations between two groups.
A two-sample t-test is utilized to compare the statistics of two samples, employing a formula that incorporates the respective sample means, standard deviations, and sizes (Thakur, 2020).
• x̄ 1 = Observed Mean of 1 st Sample
• x̄ 2 = Observed Mean of 2 nd Sample
• s 1 = Standard Deviation of 1 st Sample
• s 2 = Standard Deviation of 2 nd Sample
To determine descriptive statistics, the researcher used the Excel formula of mean, standard deviation and the variance This is done for accuracy purposes
RESULTS
Gathered Transportation Carbon Footprint Data
After conducting a survey, the results are presented in graphs illustrating the various modes of transportation utilized by students residing in the dormitory (Figure 4.1) compared to those living in their hometowns (Figure 4.2), along with the estimated distance from their homes to the school.
Figure 4.1 Different Modes of Transportation Used by Students Staying at the
Motorbike Car Bus E-bike Walking
A survey conducted among 50 students from the Advanced Education Program (AEP) residing in the dormitory reveals their preferred modes of transportation to school The results, illustrated in Figure 4.1, show that walking is the most common choice, with 10 students walking distances of 0 to 0.5 km and another 10 students covering distances of 0.5 to 1 km Additionally, 2 students use motorbikes for distances of 0 to 0.5 km, while 10 students opt for motorbikes for distances beyond 0.5 km.
1 km, and 3 students from a distance of 1 - 1.5km “E-bike” is used by 10 students from a distance of 0.5 – 1km, and 5 students from a distance of 1 – 1.5km “Car” is used by
1 student from a distance of 1 – 1.5km and “bus” is not used by any of the respondents
Figure 4.2 Different Modes of Transportation Used by Students Staying on Their
In a survey involving approximately 50 students from the Advanced Education Program (AEP) in their hometown, the results illustrated in Figure 2 reveal that "motorbike" is the predominant mode of transportation for students commuting to school Notably, 8 students were specifically highlighted in the findings.
Motorbike Car Bus E-bike Walking
A total of 24 students travel from a distance of 0.5 to 1 km, 12 students from 1 to 1.5 km, and 14 students from distances greater than 1.5 km Among them, 1 student from the 1 to 1.5 km range and 2 students from the 1.5 km+ range use a bus Additionally, transportation by car is utilized by 1 student from the 0.5 to 1 km range, 2 students from the 1 to 1.5 km range, and 1 student from the 1.5 km+ range.
“Walking” is used by 1 student from a distance of 0 – 0.5km and “e-bike” is not used by any of the respondents.
Co 2 Emission Of Students (Per Semester)
Figure 4.3 Estimated CO 2 e of Students' per Mode of Transportation (From
The total carbon dioxide equivalent (CO2e) emissions from students' transportation methods amount to 0.32 tCO2e As illustrated in Figure 4.3, the majority of these emissions, 61%, are attributed to motorbikes, followed by e-bikes at 18%, cars at 12%, and walking, which accounts for 9%.
Estimated CO 2 e of Students' per Mode of Transportation (From Dormitory)
Figure 4.4 Estimated CO 2 e of Students' per Mode of Transportation (From
The total carbon dioxide equivalent (CO2e) emissions from students' transportation modes amount to 1.93 tCO2e As illustrated in Figure 4.4, the majority of these emissions come from motorbikes, accounting for 57%, followed by buses at 29%, cars at 12%, and e-bikes at just 2%.
T-Test
The researcher gathered data from 50 students living in the dormitory and 50 students living in their hometown It is shown in Table 2, the mean of emission based
Estimated CO 2 e of Students' per Mode of Transportation (From
The transportation habits of students living in the dormitory result in a CO2e emission of 0.0055552 CO2e/km, whereas those commuting from their hometown emit 0.038648 CO2e/km The standard deviation of the squared mean for dormitory students is 0.0053285, while for students from hometowns, it is significantly higher at 0.08102713 Additionally, the variance for dormitory students is 2.839e-05, compared to 0.0065654 for those from hometowns.
The researcher calculated the sample means, standard deviations, and variances, leading to a t-value of 2.88 According to the t-table, the critical value is 1.984.
DISCUSSION AND CONCLUSION
Discussion
This study proposed that the carbon dioxide equivalent (CO2e) emissions from academic transportation differ between students residing in dormitories and those living in their hometowns Analysis using a distance-based method and t-test revealed a significant difference in CO2e emissions between the two groups of students.
A recent study reveals that students commuting from their hometowns generate significantly higher carbon emissions, with an accumulated CO2e of 1.93 tCO2e, compared to just 0.32 tCO2e for those living in dormitories This highlights the importance of employing a distance-based approach to accurately assess students' carbon footprints related to academic transportation.
In their hometown, students predominantly choose motorbikes for transportation to school, typically covering distances ranging from 0 to 1.5 km or more Conversely, students residing in dormitories prefer walking to school, also within the 0 to 1.5 km range Notably, no dormitory students opted for bus transportation, and none from the hometown chose to walk to school.
This research collected data on students' awareness of their carbon dioxide emissions and their knowledge of the topic The findings can serve as a valuable resource for future studies by other researchers.
Conclusion
At a 5% significance level, the study provides strong evidence that CO2 emissions from students commuting from their hometowns differ from those living in dormitories Students residing in their hometowns generate higher CO2 emissions due to longer travel distances by vehicles compared to their dormitory counterparts.
The aim of this study is to estimate the academic transportation carbon emission of TUAF students and to determine the most used mode of transportation of the students
This study aims to provide quantitative data for students and the school committee, despite limitations such as the absence of annual CO2e data at TUAF and the estimation of accumulated CO2e for students rather than per capita The researcher encourages future studies to address these limitations to enhance monitoring and mitigation of CO2e emissions at Thai Nguyen University of Agriculture and Forestry.
The data collected is crucial for the organization's decision-makers to assess whether students are producing excessive CO2 emissions If this is the case, the researcher suggests implementing strategies such as developing renewable energy sources, enhancing energy efficiency, and adopting sustainable transportation and urban design practices on campus To address transportation-related carbon emissions, the university could offer free bike rentals and create an app for TUAF students to calculate and track their carbon dioxide emissions.
This can be added as a requirement for students that will equate to credits or additional grades for their courses
Quantifiable data provides validation and consistency in results, making the statistics from this research reliable for addressing the specific needs of our college community This reliability instills confidence in the decision-makers at the school.
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Yanez, P., Sinha, A., and Vasquez, M (2019) Carbon Footprint Estimation in a
University Campus: Evaluation and Insight
A study by Zhou, Fu, and Cheng (2007) published in the Journal of the Air and Waste Management Association examines the emissions of light-duty gasoline vehicles in Beijing using remote sensing techniques The research highlights the effects of recent control measures on vehicle emissions, providing valuable insights into air quality management in urban environments The findings underscore the importance of monitoring and regulating vehicle emissions to improve air quality.
The researcher developed a questionnaire using Google Forms, which received approval from the supervisor The questions are designed to effectively gather the necessary data to address the research question Respondents have the option to remain anonymous by choosing not to provide their names Below are screenshots of the Google Form created by the researcher.
The researcher gathered 124 responses but due to the inefficiency of 24 responses the data is narrowed down to 100 responses, 50 of which students from the dormitory and 50 from their hometowns