This research project has been approved by the Head of the Research and Development Center for Road Transportation and Railways, Transportation Research and Development Agency, Ministry of Transportation of the Republic of Indonesia, through an approval letter (number 201/KU.004/VIII/BLTD-2019) dated August 21, 2019, issued in Jakarta. The aforementioned approval letter permits the collection of primary data from users of the Jakarta MRT Phase 1 in Jakarta, Indonesia.

A consent form was included in the questionnaire on the opening page, before respondents answered the questions. Due to the limited survey time on the train, participants gave verbal consent to participate in the study, which was approved by the ethics committee. There was no external influence or coercion on participants to participate in this study. In addition, all data collected will be kept confidential to maintain the privacy of the participants.

This research was conducted on the MRT Jakarta Phase 1 operational line, which has a length of 16 km, starting from HI Bundaran Station and ending at Lebak Bulus Station. The MRT Jakarta Phase 1 line includes elevated structures and underground lines, which comprise 13 stations. Of these, seven are elevated stations and six are underground stations. In Figure 1 shows The elevated MRT line consists of the following stations: Lebak Bolus, Fatmawati, Cipete Raya, Haji Nawi, Blok A, Blok M, and Sisingamangaraja. The underground line consists of the following stations: Senayan, Istora, Bendungan Hilir, Setiabudi, Dukuh Atas, and Bundaran HI ( PT. MRT Jakarta, 2024).

The survey was conducted over 7 days, including weekdays and weekends. The travel patterns of MRT users on weekdays differ from those observed on weekends. On weekdays, the MRT is primarily used for work-related commutes, whereas on weekends, it is used more frequently for non-work purposes.

This study employed a combination of primary and secondary data sources. Secondary data were obtained through a comprehensive review of relevant literature and information from relevant agencies. Primary data were collected by administering questionnaires to MRT users and by direct observation. The sample was determined in accordance with the Isaac and Michael formula, which is a statistical method for calculating the minimum sample size for a given population. S = χ 2 NP ( 1 − P ) d 2 ( N − 1 ) + χ 2 P ( 1 − P ) (1)

In this context, variable S represents the number of samples to be determined, and N denotes the known population size. The chi-squared statistic, denoted by χ2, is a function of the level of freedom and error rate. The variable d represents the difference between the sample and the population means. The probability of being correct (P) is a function of the aforementioned variables, whereas the probability of being wrong (Q) is a function of the variables N and S ( Sugiyono, 2016).

In 2019, the daily ridership of the MRT system was 86,270 passengers. The calculated value of the chi-square statistic, with one degree of freedom 1 and a 5% margin of error, is 3.841. This indicates that the probability of the observed result is 0.5. Based on this parameter, a minimum sample size of 382 was determined for statistical significance. The survey was conducted inside the train due to the relatively short waiting time of only 5 minutes at the station. To ensure accurate data collection, respondents were marked as passengers boarding from the station where they first boarded the train. Furthermore, the data obtained was processed using IBM SPSS Statistics 27 software ( IBM, 2024).

In this study, validity and reliability tests were conducted on the variables included in the questionnaire distributed to MRT user respondents. The trial was conducted with a sample of 50 MRT users. The variables addressed in this study encompass respondents’ profiles and those related to using MRT Jakarta Phase 1. The questionnaire included questions regarding the respondents’ characteristics, including age, education level, current occupation, and income. The variables pertaining to the use of the MRT system encompass the preceding modes of transportation used by respondents before they adopted the MRT, the rationale behind their chosen mode of transport, the frequency of their weekly trips, and the number of daily trips.

The instrument reliability test uses the Cronbach’s alpha value as the reliability coefficient. If the reliability coefficient value is equal to or greater than 0.6, the instrument has good reliability; if the reliability coefficient value is <0.6, then the tested instrument is not reliable ( Arikunto, 2017).

Table 1 showing The validity test results indicate that all variables were statistically significant at a significance level of 0.05 or below. The reliability test results for the variables in question indicate that a Cronbach’s alpha value of 0.69 is more significant than 0.6, demonstrating reliability

This study employed a descriptive analytical methodology, whereas the calculation of CO ₂ emission reductions was based on the simplified CDM ACM 0016 methodology. A descriptive approach was employed to examine the demographic and socioeconomic characteristics of MRT users, including gender, age, income, and occupation. The methodology employed to quantify CO ₂ emission reductions resulting from the switching of private transportation to MRT systems is that delineated in the simplified Clean Development Mechanism (CDM) ACM 0016 ( UNFCCC, 2015).

The CDM ACM 0016 methodology was designed for the development and operation of rail- or bus-based mass transit systems (MRTS) in urban or suburban areas. This is achieved through the following formula: E R y = B E y − P E y (2)

Where:

ER _y = Emission reduction in year y (tons of CO ₂)

BE _y = Baseline emissions in year y (tons CO ₂)

PE _y = Emission reduction by MRT Project in year y (tons CO ₂)

The calculation of emission reductions excludes the consideration of the linkage of emissions in the year in question (LEy) due to the substantial resources required for such a survey. The baseline scenario considers the emissions resulting from using road transportation modes, specifically cars and motorcycles, in the absence of MRT. These emissions are differentiated according to the type of road transportation mode and are calculated based on data from surveyed passengers.

The baseline emissions were calculated using the following formula: B E y = P y P SPER ∑ p ( B E p , y × FE X p , y ) (3)

Where:

BE _y = Baseline emissions in year y (t CO ₂)

BE _p,y = Baseline emissions per surveyed passenger in year y (t CO ₂)

FEX _p,y = Expansion factor per surveyed passenger per year (y)

P _y = Total number of passengers per year y

P _SPER = Number of passengers during the survey period (1 week)

p = Passenger survey (per passenger)

y = Year

The baseline emissions per passenger were calculated using the following formula: B E p , y = ∑ i BT D p . iy × E F pkm , iy × 10 − 6 (4)

Where:

BE _p,y = Baseline emissions per surveyed passenger in year y (t CO ₂)

EF _pkm,i,y = Emission factor per passenger kilometer per transport mode in year y (g CO ₂/pkm)

BTD _p,i,y = Baseline mileage per surveyed passenger using transit mode I in year y (pkm)

The vehicle categories identified use fuel oil and operate on roads parallel to the MRT Jakarta Phase 1 line.

The emissions generated by the MRT project were calculated using the following formula: P E y = DP E y + IP E y (5)

Where:

PE _y = projected emissions in year y (t CO ₂)

DPE _y = Project direct emissions in year y (t CO ₂)

IPE _y = Indirect emissions of the project in year y (t CO ₂)

The direct emissions of the project in year y (t CO ₂) are calculated using the following formula: DPE y = EC y × E F E I , y (6)

Where:

DPE _y = Direct project emissions in year y (t CO ₂)

EC _y = Electricity consumption (only for MRT traction) per year y (MWH)

EF _El,y = Electricity Grid Emission Factor (kg CO ₂/kWh).

Indirect project emissions (IPEy) are calculated using the following formula: IPE y = P y P SPER ∑ P ( IPE p , y × FEX p , y ) × 10 − 6 (7)

Where:

IPE _y = Project indirect emissions in year y (t CO ₂)

IPE _p,y = Project indirect emissions per surveyed passenger p in year y (g CO ₂)

FEX _p,y = Expansion factor for each surveyed passenger (p) in year y

P _y = Total number of passengers per year y

P _SPER = Number of passengers during the survey period (one week)

P = surveyed passengers (each individual)

y = year of the credit period

The mitigation action strategy implemented in this methodology is to reduce GHG emissions by enhancing energy efficiency and redirecting users of high-GHG transportation modes to those that produce fewer GHG emissions ( UNFCCC, 2015). In this study, the methodology is streamlined to facilitate its implementation in accordance with the availability of data and traffic conditions on the road sections where the MRT Jakarta is constructed. This is detailed in the Results and Discussion section.

This study’s estimation of CO ₂ emission reductions by the CDM approach for mass transit without feeder transport. This is because during the study period, the MRT system was not integrated with the feeder transportation network. The analysis is primarily concerned with emissions from road transportation, focusing on reducing emissions from private vehicles due to a shift in modal preference toward MRT. The scope of the emission calculation covers a range of private transportation modes, including motorcycles and cars. The study did not analyze public transportation users opting to use MRT. This is because mass transit operations, such as MRT, alleviate congestion by incentivising individuals to transition from private vehicle use. This is particularly relevant given that private vehicles are associated with increased vehicle exhaust emissions due to prolonged low vehicle speeds.

In this study, the calculation of emission reduction using the CDM ACM 0016 method was simplified by excluding three variables for the following reasons: 1.

Indirect emissions reductions derived from carbon dioxide (CO ₂) emissions from the point of origin to the railway station and from the station to the final destination are not included in the calculation because they have no impact on emissions reductions along roads parallel to MRT operations. The CO ₂ emission reductions calculated in this study are exclusively associated with emissions reductions along roads parallel to the MRT corridor.

The simplified ACM 0016 CDM formula used in this study is outlined in Table 4.

It is also noteworthy that there is no measurement of carbon emissions at the upstream level, which refers to emissions originating from power plants. This study focused exclusively on emissions along the MRT route and the electricity consumption attributed to MRT operations along this route.

In calculating the baseline CO ₂ emissions, the following factors were considered: the CO ₂ emissions generated by private transportation users who switch to MRT Jakarta Phase 1. Table 5 showing the onboard survey results indicate that 53.75% (or 258 respondents) of private transportation users switched to using the MRT in the road corridor parallel to the MRT. This figure shows 50 private car users, 25 motorcycle users, 71 online car users, and 112 online motorcycle taxi (Ojek Online) users. Online one and online cars are included in the category of private transportation, as they represent the conversion of private vehicles into online public transportation. The derived baseline accounts for emissions generated by private transportation in scenarios where MRT is not used, based on data obtained from surveyed passengers.

The calculation in Table 5 assumes that private transportation users use premium fuel, which has a local emission factor of 72,967 kg CO ₂/TJ (72,967 g CO ₂/TJ). The baseline emissions for the use of private transportation that switches to using MRT, as calculated from the survey data, are 169,625.6 g CO ₂ per day or 169.8 kg CO ₂ per day for one trip. If two round trips are completed, the baseline emissions for the MRT corridor are 339.3 kg CO ₂ per day.

It is assumed that the percentage of private transport users (53.8%) represents the percentage of the population that transfers to MRT. In 2019, the total number of MRT users during the 9-month period or 275 days) was 23,637,960 passengers. This figure can be used to calculate the number of private transportation users who moved to MRT, which comprised 12,705,404 passengers per year. Based on these assumptions, the baseline emissions of all private transport users who shifted to MRT in 2019 were 16,706,684.4 kg CO ₂ or 16,706.7 tons CO ₂ (equivalent to 16,706,700 kg CO ₂) as shown in Table 6.

The data from P.T. MRT Jakarta indicate that the electricity consumption for MRT traction over the eight-month period from May to December 2019 was 14,448,000 kWh, which is equivalent to 14,448 MWh. This equates to an average consumption of 0.6795 kWh per passenger, resulting in a total electricity consumption of 16,062,106 kWh for 2019. It has been established that the electricity network emission factor for the DKI Jakarta area in 2019, as calculated by the Ministry of Energy and Mineral Resources, is 0.87 tons of CO ₂ per MWh (ESDM, 2019). Based on these calculations, the direct emissions of the MRT project in 2019 were 13,974.03 tons of CO ₂. This is shown in Table 7.

The reduction in CO ₂ emissions resulting from the start of operations for MRT Jakarta Phase 1 can be quantified by subtracting the baseline emissions from the emissions associated with the MRT Jakarta Phase 1 project. This reduction can be achieved through a transition from private transportation to MRT use. The baseline emissions of 16,706.7 tons of CO ₂ are subtracted from the MRT project emissions of 13,974.03 tons of CO ₂, resulting in a reduction value of 2,732.7 tons of CO ₂ in 2019. Thus, the reduction in CO ₂ emissions resulting from the shift from private transportation to MRT averaged 227.7 tons of CO ₂ per month, with a daily average of 9.9 tons of CO ₂ in 2019 . This is shown in Table 8.

In this study, the authors predicted 2023 using the above formula and leveraging data derived from the 2019 survey outcomes. Predictions are made to determine the emission reduction impact of MRT construction and operation if there is an increase in private vehicles that switch to using MRT. The following assumptions are used: a.

The average mileage of private cars using the survey results was 19.7 km and that of motorcycles was 17.3 km.

In this study, the aforementioned formula is used to project emission reductions in 2023 using data derived from the 2019 survey results. 2023 represents the normalization period after the COVID-19 pandemic in Indonesia. As a result, there is a significant increase in MRT ridership in 2023, as the previously imposed capacity restrictions have been lifted. Therefore, a growth in CO ₂ emission reduction resulting from the operation of MRT Jakarta Phase 1 can be observed compared with the 2019 data. The following assumptions were used to estimate CO ₂ emission reductions in 2023: a.

The mean distance traveled by private car passengers was 19.7 km, while the mean distance traveled by ‘car online’ passengers is 17.8 kilometers. The mean distance traveled by motorcycle passengers was 17.3 km, and the mean distance traveled by ‘ojek online’ passengers is 17.2 kilometers. These figures are derived from the findings of a survey conducted in 2019.

To establish a baseline for prediction, the shifting of private vehicles in 2019 was taken as the point of departure. A review of the survey data indicates that the shift toward private transportation totaled 53.75%. If 2023 is assumed to have the same shifting conditions as 2019, then the emission reduction can be estimated as follows: 1.

The baseline carbon dioxide emissions per day, calculated based on survey data, were found to be 339.3 kg CO ₂ per day.

According to data provided by PT MRT Jakarta, the total number of passengers using the MRT system during the nine months from January to September 2023 was 24,020,222. The calculation indicates that 12,860,877 individuals use private transportation users is 12,860,877 individuals. Furthermore, it can be established that the baseline emission in 2023 is 16,976.9 tons of CO ₂ as shown in Table 9.

It is established that the electric traction power in 2023 is 12,567 MWH. When this figure is multiplied by the power grid emission factor of 0.87, the direct emissions of the MRT project in 2023 are 10,932.99 tons of CO ₂, as shown in Table 10. The reduction in baseline emissions, in conjunction with the project emissions, resulted in an overall reduction of 6,043.9 tons of CO ₂ in 2023, as illustrated in Table 11. Subsequent calculations can be performed using the same assumptions by first predicting the number of users and the electric traction power for each year. This methodology allows the projection of the total number of users and electric traction power for each year in the future.

The results of this study indicate that 53.75% of MRT Jakarta Phase 1 users transitioned from private transportation, leading to a reduction in CO ₂ emissions of 2,732.7 tons in 2019 and 6,043.9 tons in 2023. The value of CO ₂ emission reductions produced in this study are comparatively lower than those reported by the DKI Jakarta Provincial Environment Office, which documented emission reductions from the MRT Jakarta Phase 1 project resulting from the transition of private transportation to MRT in 2019, amounting to 6,151 tons of CO ₂ ( DKI Jakarta Provincial Environment Office, 2019). Ini 2022, the implementation of MRT Jakarta Phase 1 resulted in a reduction of 2,104 tonnes of CO ₂ emissions ( DKI Jakarta Provincial Environment Agency, 2023).

The discrepancy can be attributed to the different methodologies used in the calculations. The emission calculations reported by the DKI Jakarta Environment Agency employ the Tier I approach. This approach only considers vehicle fuel consumption, assuming that MRT users switch from private transportation. It does not account for the emissions generated by MRT operations. Various methodologies are employed to quantify CO ₂ emissions associated with mass transportation, with notable discrepancies in specific factors incorporated into calculations. Accordingly, Fontaras et al. stated that In order to determine the quality of monitoring, reporting, and reported emissions reductions, CO ₂ certification procedures are required ( Fontaras, Zacharof, & Ciuffo, 2017).

This research shows that not all MRT Jakarta Phase 1 users replaced private transport with public transport. Many also switched from other public transportation modes. Therefore, it is not appropriate to calculate CO ₂ emissions reductions by assuming that all MRT passengers will replace private transport with public transportation. Calculating emissions reductions using CDM ACM 0016 shows that the greater the number of private transport users who switch to MRT, the greater the potential CO ₂ reductions. Conversely, if the number of MRT users decreases, the MRT project may increase CO ₂ emissions. This phenomenon is due to an increase in electricity consumption per passenger, resulting in an increase in emissions associated with electricity generation. The operation of MRT Jakarta Phase 1 still relies on electricity from coal-fired power plants, which have high emission factors. These findings are in agreement with the finding that switching from private transport to MRT does not result in CO ₂ benefits. Therefore, efforts to increase MRT ridership and energy conservation initiatives are needed ( Sharma, Singh, Dhyani, & Gaur, 2014). It is also recommended that non-motorized modes of transport, such as walking and cycling, be prioritized for short distances, in line with research that suggests reducing the consumption of natural resources, especially physical space and energy and their environmental impacts. For certain distances that require motorized transport, modes that offer higher capacity and better energy efficiency, such as high-capacity collective transport modes, such as urban trains, subways, and busses, should be used ( D’Agosto, 2019). Therefore, the construction of MRT in Jakarta is very appropriate, as evidenced in this study that more than 50% can attract private transport users to switch.

The paradigm of using the CDM methodology, which is quite difficult because it requires a lot of data ( Asian Development Bank, 2013), and the difficulty of determining the baseline in the transport sector ( Michaelowa, et al., 2020), especially for the MRT project, can be overcome by simplifying the CDM ACM 0016 in this study. However, there is still a need for a primary survey to obtain factual data on the modes of transport switching to MRT.

The strength of this research is that the calculation of CO ₂ emission reductions from the operational impacts of MRT Jakarta Phase 1 used the CDM ACM 0016 method, which is a calculation used by the IPCC and recommended by the UNFCCC to obtain carbon certification that can be used in world carbon trading. This method is simplified for use in Indonesia. The advantage of using this method is that the calculation of CO ₂ emission reductions from MRT operations is carried out using primary surveys; thus, private transportation users who switch to MRT can be identified. Second, the calculation also includes the distance traveled for each type of vehicle and fuel consumption. Third, the calculation also includes the electricity emissions generated from MRT operations.

In this study, the CDM ACM 006 method is simplified because it does not include measuring emission reductions that occur from the origin to the starting station and from the end station to the final destination, shifting MRT users from other modes of public transportation, and emission reductions on other roads affected by MRT operations. Future research can address these shortcomings to improve the results of this study.

This research project has been approved by the Head of the Research and Development Center for Road Transportation and Railways, Transportation Research and Development Agency, Ministry of Transportation of the Republic of Indonesia, through an approval letter (number 201/KU.004/VIII/BLTD-2019) dated August 21, 2019, issued in Jakarta. The aforementioned approval letter permits the collection of primary data from users of the Jakarta MRT Phase 1 in Jakarta,Indonesia.

A consent form was included in the questionnaire on the opening page, before respondents answered the questions. Due to the limited survey time on the train, participants gave verbal consent to participate in this study which was approved by the ethics committee. There was no external influence or coercion on participants to participate in this study. In addition, all data collected will be kept confidential to maintain the privacy of the participants.