Mobility and Associated CO2 Emissions During and After COVID-19: A Case Study in Indonesia

differences in travel distances, trips, and transportation-related CO2 emissions between COVID-19 and the post-COVID-19 situation needs to be carried out. After that, changes in people's travel trends need to be studied so that the study results could become input for transportation policies. Abstract Changes in transportation trends can occur during and after COVID-19, such as travel distance, trip, and choice of transportation mode. The positive benefits from these changes in transportation trends should be maintained, to reduce disaster risk of environmental hazards from the CO2 emissions. Research on changes in mobility, trips, and CO2 emissions during and after COVID-19 in Indonesia is still very limited; whereas, changes in these transportation variables can be an inspiration for determining sustainable transportation policies in the future. This study aims to compare amid COVID-19 and post-COVID-19 transportation variables—travel distances, trips, and associated CO2 emissions. This research was conducted by giving questionnaires to 400 participants in Aceh Besar District. The questionnaire contains questions regarding the distance traveled, trips, fuel spent, and socio-economic characteristics. The CO2 emissions were calculated using IPCC (2006). The travel distance, trips, and transportation-related CO2 emissions during and after COVID-19 were compared respectively based on statistics. The results showed that there were significant differences in travel distances, trips, and transportation-related CO2 emissions between COVID-19 and the post-COVID-19 situation in the District of Aceh Besar. This article also presents several recommendations based on the data analysis results linked to literature studies about the sustainability of transportation as a result of COVID-19 situation; those are: providing quality public transportation, considering teleworking, providing solutions for workers who do not have access to online work, and paying attention to increasing use of private cars and car sharing post COVID-19.


Travel behavior
Usually, the destination of people's daily trips is outside the residential area. Thus, the overall trip distance tends to be more influenced by the distance between the residence and the main destination of the trip. However, the travel distance is also influenced to a small extent by the local built environment, such as the distance of bus stops in an area, the availability of parking locations, and the availability of local grocery stores (Mouratidis et al., 2019;Ewing & Cervero, 2010;Naess, 2012;Naess et al., 2019;Stevens, 2017).
In light of the foregoing, studies around the world have found that residents of suburban neighborhoods far from the city center have greater overall commuting distances than residents of the city center. This keeps residents of suburban neighborhoods far from the city center commuting by car. The proportion of car trips by suburban residents is higher than the proportion of car trips by city dwellers (Ewing & Cervero, 2010;Naess, 2012;Stevens, 2017). Geng et al. (2017) explained the relationship between psychological factors and travel behavior. Desire and emotions may not have a significant influence on all green behaviors, but they do become important indicators for people's car usage behavior (Geng et al., 2016;Hounsham, 2006). Morris & Guerra (2015) and Lancée et al. (2017) found that car passengers and car drivers both experienced more positive emotions; conversely, public transport users experience the most negative emotions. This is what might cause vehicle users who do not care about sustainability and do not transport sustainably to be more dependent on car use. Convenience is a dominant motivation of people who depend on the automobile mode. They seek less time, less effort, pleasurable emotions, social status, identity, and positive self-image (Geng et al., 2016;Hounsham, 2006;Steg, 2005;Zailani et al., 2016).

Sustainable transportation
In addition to groups that do not have a green transportation vision and behavior, Geng et al. (2017) also introduce a group that does not have a green transportation vision but is forced to use green transportation at this time.
This group often has many travel motives and is easily influenced by other non-environmental factors. The main motive of this group in choosing a mode of transportation is the economy. After that, comfort, convenience, safety, and lastly environmental protection are other minor factors that influence them to choose a mode of transportation. Currently, this group is passively choosing the cheaper and green mode of travel. Once the economy allows, travel goals such as comfort and convenience will quickly become the main motive of this group. Geng et al. (2017) also found a third group, whose vision and behavior are compatible with sustainable transportation. This group has positive attitudes to government measures. Their economic conditions and proenvironmental motives are consistent, resulting in sustainable green travel behaviors.
As a result, comfort and convenience motives will determine behavior more. Therefore, policies on limiting the use of cars and the publication of information on environmentally friendly transportation are not enough to shape eco-friendly travel behavior (Geng et al., 2017).
In addition, health factors can also be the reason people use sustainable transportation (Jones & Ogilvie, 2012).
Occasional increases in physical activity are often associated with walking, cycling, and public transportation use.
Therefore, health motives can be important catalysts or cofactors to promote people's green travel behavior (Geng et al., 2017).

COVID-19
COVID-19 first appeared in December 2019 in Wuhan, the capital city of Hubei province in China. Wuhan was also the first epicenter of the outbreak (Zhou et al., 2020). Three months after its appearance, the virus has affected 91 143 countries. Thus, the World Health Organization (WHO) upgraded the status of the outbreak to a pandemic, which means the severity of the virus is worrying (WHO, 2020b).
COVID-19 has led to policies asking people to limit their travels, stay at home, and not hold activities that have a large number of participants (WHO, 2020a). This is because several factors, such as accessibility, climate, pollution, or the preparedness of the local health system, are the causes of the spread of COVID-19. For example, in Europe, the biggest victims of the first wave of the COVID-19 pandemic were areas with large, connected lands, high levels of pollution, a colder and drier climate, and a relatively poor health system (Rodríguez-Pose & Burlina, 2021).
In addition to policies by the government, changes in people's behavior during COVID-19 are also due to communication and advice within the family (Fadmawaty & Wasludin, 2021). In the early days of COVID-19, travel restriction policies were not implemented in Aceh (Sufri et al., 2021), but then people's understanding of the dangers of COVID-19 made them voluntarily limit their travels (Mukhlis et al., 2022).

Teleworking
In the context of work, work from home is widely applied during COVID-19 to create social distancing. The practice of working from home was first introduced in the early 1970s with the term telecommuting (Nilles, 1975), and then developed with various terms, such as remote working (Hardill & Green, 2003), teleworking (Alizadeh, 2012), and work at home (Baruch & Yuen, 2000;Himawan et al., 2022).
Working from home means combining work location and daily life location to optimize time, space, etc. Based on historical searches, work from home existed in the Middle Ages. At that time, people traded and opened shops in their homes (Applebaum, 1992;Hamada, 1993), so the rooms in the house were multifunctional. Then, activities in the office began to appear during the industrial revolution. At that time, workers had to go to work locations and offices to work. Thus, workers begin to work inflexibly at work sites that contain special work equipment.
More flexible work arrangements then emerged when many women were working (Schonberger, 1971); it was at this time that the concept of working from home began to reappear (Himawan et al., 2022). Experts are still debating the efficiency of driving restrictions. Economists, in general, believe that congestion rates are effective in reducing the number of cars (K.A. Small & Gomez-Ibañez, 1998;Small & Verhoef, 2007). However, in reality, people are unwilling to pay the higher price, even though they will benefit from lower congestion (Rouwendal & Verhoef, 2006). Meanwhile, rationing policies are also considered fairer than road pricing, to reduce road congestion (De Grange & Troncoso, 2011;Rouwendal & Verhoef, 2006); this is because the same restrictions apply to every car owner. Thus, people do not easily accept the congestion tariff strategy (Schade & Schlag, 2003;Schuitema et al., 2010). Instead, driving restrictions are more accepted by society and the government. In 2007 and 2008, Beijing introduced a policy of limiting odd-even license plates, and this policy proved to reduce congestion and pollution (Y. Wang et al., 2009).

Travel restriction
Driving restriction policies, however, are claimed by some researchers to be unfair and inefficient (De Grange & Troncoso, 2011). The expert reasoned that people might disobey, buy a second car, or take longer trips on the days they can travel. In addition, driving restrictions are also considered not to apply the concept of willingness to pay (De Grange & Troncoso, 2011). Residents who do not have good access to public transportation nodes have less opportunity to change their mode from car to public transportation. As a result, they are more likely to disobey the driving restrictions. Moreover, public transport systems in most developing countries are not in good condition. Thus, people who are in such conditions may buy a second car to get around the policy of restricting driving (H. Goddard, 1997;H. C. Goddard, 1999). Based on the realities in Mexico City, people with higher incomes buy a second car with a different license plate to get around the no-driving policy. Thus, the number of vehicles in Mexico City actually increased (Eskeland & Feyzioglu, 1997). These additional cars are usually older, and therefore not environmentally friendly (Eskeland & Feyzioglu, 1997). Gallego et al. (2012) also found a similar phenomenon in Mexico and Santiago. Carbon dioxide per hour in Mexico and Santiago is increasing because people are buying second cars to get around the driving restriction policy. In addition, travel distances can be increased because roads are no longer congested, and car users are able to travel more quickly than before the driving restrictions policy was implemented (Downs, 2004).

Methods
There were five steps in this research. First, the number of population and samples were calculated. Second, the travel distance, trips, transportation-related fuel usage, and socioeconomic data were collected through a survey.
Third, the transportation-related CO2 emissions were calculated using IPCC (2006). Fourth, the travel distance, trips, and transportation-related CO2 emissions during and after COVID-19 were compared respectively based on statistics. Fifth, the research results are linked with suggestions from related literature.

Population and samples
The area of study was the District of Aceh Besar in the Province of Aceh, Indonesia. The population was the households in the District of Aceh Besar. The number of samples was calculated using Slovin formula (Eq. 1). The 5% of acceptable margin of error (e) was applied in this research. The number of the household (N) in the District of Aceh Besar was 60,112; as a result, the number of samples was as follows: The survey Proportionate stratified random sampling was applied in this research. This technique is used to determine the sample in the population that is not homogeneous. Stratified random sampling is a subset of probability sampling techniques (Sugiyono, 2012). For this reason, the sub-district household was selected randomly. The participant data obtained through the survey was travel distance, trips, transportation-related fuel usage, and socioeconomic characteristics.

CO2 emissions estimation
The IPCC (2006) formula, as presented in Eq.
(2), was used to estimate the CO2 emissions. CO2 emissions were obtained by multiplying the fuel magnitude (fuel) and emission factor (EF).

Statistical comparison
The parametric statistical test-paired sample t-test-was applied in this research (Ross & Willson, 2018). This test was applied because the compared variables were interrelated.

Results
The participants' socioeconomic data is presented in Table 1. Regarding the gender, 48% of participants were male; 52% were female. Regarding the age of the participants, 1% of the participants were aged 17-19 years, 53% of the participants were aged 20-29 years, 33% of the participants were aged 30-39 years, 11% of the participants were aged 40-49 years, and 2% of the participants were aged 50-59 years.
Regarding participant education, 13% of participants had basic education, 13% of participants had diploma education, 61% of participants had bachelor's education, and 13% of participants had postgraduate education.
Regarding transportation mode, 57% of participants traveled by motorcycle, and 43% of participants traveled by car. The travel distance, trips, and transportation-related CO2 emissions data was presented in Table 3. The average travel distance during and after COVID-19 was 81.9 and 127.9, respectively. The average trips during and after COVID-19 were 25.4 and 25.9, respectively. The average transportation-related CO2 emissions during and after COVID-19 were 42.7 and 47.3, respectively.

Statistical comparison
Based on the paired sample t-test, the travel distance during and after COVID-19 was statistically different. The trips during and after COVID-19 were also statistically different. The transportation-related CO2 emissions during and after COVID-19 were statistically different as well. The statistical comparison is presented in Table 4.

Discussion
People travel distances in the District of Aceh Besar during and after COVID-19 were statistically different. The commuter's number of trips in the District of Aceh Besar during and after COVID-19 also differed statistically.
The commuter may reduce mobility during COVID-19 due to travel restrictions. The number of trips may decrease due to the implementation of telework (Olde Kalter et al., 2021). Telework could be performed by high-income people (Olde Kalter et al., 2021). Low-income workers in most cases also reduce their number of trips, but only to a lesser extent, due to lower access to telework and private vehicles (Beck & Hensher, 2020). In addition, trips may be reduced because people are concerned about traveling with other people in the same vehicle (Vega-Gonzalo et al., 2023).
The commuters may increase their mobility due to the easing of travel restrictions after COVID-19; on the contrary. In addition, people who used to travel by public transportation or shared vehicles may purchase private vehicles during COVID-19 due to fears of spreading the virus in public transportation or shared vehicles (Basu & Ferreira, 2021). The phenomenon of this increase in private vehicles could increase post-COVID-19 mobility.
In this study, the difference between travel distances during and after COVID-19 was larger than the difference in trips during the same period. The reason for this is that people might stop their main trip-traveling to workbecause they were doing teleworking activities. By not commuting to work, travel distances will decrease substantially. However, older people usually do not have access to teleworking, online shopping activities, and online social activities. As a result, older people tend not to change their trips to work and other substantial trips during COVID-19. Workers in essential sectors such as hospitals, the energy sector, staple food stores, and the sanitation sector, also did not cancel their trips to work in the office during COVID-19 (Politis et al., 2021). In addition, This variation in the implementation of trips to the office may have caused a relatively small difference between trips during and after COVID-19.
Sustainable transportation meets the mobility needs of people in a way that is least damaging to the environment and does not damage the mobility needs of the next generations (Rodrigue, 2020 The transportation sector contributes 28% of the total greenhouse gas emissions produced by various sectors. Transportation-related emissions from burning fossil fuels have increased by 19% from 1990 to 2021 (EPA, 2023).
If transportation-related CO2 emissions do not decrease, then these CO2 emissions will cause global warming.
The reduction in transportation-related CO2 emissions during COVID-19 should be maintained so that global warming can be prevented. Vega-Gonzalo et al. (2023) suggest several recommendations so that post-COVID-19 sustainable transportation can continue. First, quality public transportation should be provided. Second, teleworking should be continued.
Third, workers who do not have access to teleworking should be assisted through appropriate policies. Fourth, attention must be paid to people who switch their mode of choice from public transportation to private vehicles during and after COVID-19. Finally, COVID-19 can change people to both reduce or increase their trips by using shared vehicles.

Conclusions
The mobility-travel distance and trips-of people of Aceh Besar District during and after COVID-19 differed significantly. CO2 emissions due to the transportation of Aceh Besar District people during and after COVID-19 were also statistically different. This shows that the transportation of people of Aceh Besar District during COVID-19 was more sustainable than after COVID-19. This sustainable transportation should be maintained. Previous literature provides several suggestions to maintain sustainable transportation after COVID-19. The recommendations are providing quality public transportation, considering teleworking, providing solutions for workers who do not have access to online work, and anticipating increased use of private and shared vehicles. In addition, studies that compare transportation behavior before, during, and after COVID-19 should be carried out.