Simplification of clean development mechanism to measure CO<sub>2</sub> emission reductions from shifting private transportation to mass rapid transit: a case study of MRT Jakarta Phase 1

Nunuj Nurdjanah; Tri Edhi Budhi Soesilo; Kosuke Mizuno; Raldi Hendro Koestoer

doi:10.12688/f1000research.155406.1

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Research Article

Simplification of clean development mechanism to measure CO₂ emission reductions from shifting private transportation to mass rapid transit: a case study of MRT Jakarta Phase 1

[version 1; peer review: 1 approved with reservations]

Nunuj Nurdjanah ^1,2, Tri Edhi Budhi Soesilo¹, Kosuke Mizuno¹, Raldi Hendro Koestoer¹

PUBLISHED 18 Sep 2024

Author details Author details

¹ School of Environmental Science, Universitas Indonesia, Jakarta Pusat, West Java, 10430, Indonesia
² Transportation Policy Agency, Ministry of Transportation, Jakarta Pusat, DKI Jakarta, 10110, Indonesia

Nunuj Nurdjanah
Roles: Conceptualization, Data Curation, Formal Analysis, Funding Acquisition, Investigation, Methodology, Project Administration, Resources, Software, Supervision, Validation, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing

Tri Edhi Budhi Soesilo
Roles: Conceptualization, Formal Analysis, Methodology, Project Administration, Resources, Supervision, Writing – Review & Editing

Kosuke Mizuno
Roles: Conceptualization, Formal Analysis, Methodology, Project Administration, Resources, Supervision, Writing – Review & Editing

Raldi Hendro Koestoer
Roles: Conceptualization, Formal Analysis, Methodology, Project Administration, Resources, Supervision, Writing – Review & Editing

OPEN PEER REVIEW

REVIEWER STATUS

Abstract

Background

The Indonesian government built the Mass Rapid Transit (MRT) in Jakarta to reduce traffic congestion and carbon dioxide (CO2) emissions. The objective of this study is to estimate the CO2 emissions reductions from switching from private transport to MRT by using a methodology proposed by the United Nations Convention on Climate Change (UNFCCC) for developing countries, namely the Clean Development Mechanism (CDM) methodology, which generates Certified Emission Reductions (CERs). This methodology is more comprehensive than other available methodologies. However, this method has not been widely used to calculate greenhouse gas (GHG) emission reductions in mass transit projects because it is complex enough to require a lot of data based on primary surveys. Therefore, this research simplifies the CDM formula to make it easier and applicable in Indonesia.

Methods

The primary data were collected using a questionnaire distributed to 480 MRT Jakarta Phase 1 user respondents in September 2019 (baseline); the secondary data were obtained from The MRT Jakarta. The data were processed using IBM SPSS Statistics 27. The simplified Clean Development Mechanism Approved Consolidated Methodology 0016 (CDM ACM 0016) was the analysis method used.

Result

The results of this study indicate that 53.75% of MRT Jakarta Phase 1 users are shifting from private transportation, which has reduced CO2 emissions by 2,732.7 tons in 2019 and 6,043.9 tons in 2023. Increasing the number of MRT passengers who switch from private transportation will further reduce CO2 emissions.

Conclusion and implications

The simplified CDM ACM 0016 formula may be suitable for use in Indonesia. This would enable the measurement of CO2 emission reductions through mitigation actions through MRT development to be certified by the UNFCCC. Applying this method in calculating CO2 emission reductions, implementing strategies to increase MRT passengers, and using renewable energy electricity sources would increase CO2 emission reductions.

Keywords

Mass Rapid Transit (MRT), Greenhouse Gas, Shifting, Private Transportation, Clean Development Mechanism

Corresponding author: Nunuj Nurdjanah

Competing interests: No competing interests were disclosed.

Grant information: The author(s) declared that no grants were involved in supporting this work.

Copyright: © 2024 Nurdjanah N et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

How to cite: Nurdjanah N, Soesilo TEB, Mizuno K and Koestoer RH. Simplification of clean development mechanism to measure CO₂ emission reductions from shifting private transportation to mass rapid transit: a case study of MRT Jakarta Phase 1 [version 1; peer review: 1 approved with reservations]. F1000Research 2024, 13:1069 (https://doi.org/10.12688/f1000research.155406.1) First published: 18 Sep 2024, 13:1069 (https://doi.org/10.12688/f1000research.155406.1) Latest published: 18 Sep 2024, 13:1069 (https://doi.org/10.12688/f1000research.155406.1)

Introduction

The world’s highest greenhouse gas contributor is CO₂ emissions in the transportation sector, which account for approximately 20% of global emissions, with the majority of these emissions resulting from land transportation (Albuquerque, Maraqa, Chowdhury, & Mauga, 2020). It is estimated that as much as 91% of transport emissions originate from road transport (Sekaryadi & Santosa, 2017). Projections indicate that transportation will be the most rapidly growing source of emissions over the next three decades, particularly in developing countries. The proportion of greenhouse gas (GHG) emissions from transportation in developing countries is projected to increase from approximately 35% in 2000 to 52% to 63% by 2030. Therefore, measures must be taken to reduce carbon dioxide and related greenhouse gas emissions. In this sector, several options are available for consideration, including the promotion of vehicles that use clean technologies and fuels, demand management, the implementation of more efficient public transportation systems, and the transition to cleaner modes of transportation (Asian Development Bank, 2013). However, countries’ patterns and behaviors vary considerably in implementing CO₂ emission reduction efforts and targets (Nihayah, Gravitiani, & Tri Rahayu, 2021).

According to data published by the World Resource Institute (WRI), Indonesia became the sixth most significant carbon GHG emitter in the world in 2018, followed by China, the United States, the European Union, India, and Russia (WRI, 2019). The results of the calculation of national carbon dioxide (CO₂) emissions indicate that Indonesia’s greenhouse gas (GHG) emission level in 2018 was 1,615,569 Gg CO_2e (Central Bureau of Statistics, 2020). A total of 144,169 Gg tons of CO₂ were produced by the transportation sector, comprising the land and rail transportation subsectors, which collectively emitted 130,748 Gg tons of CO₂ (90.7%), the sea transportation subsector, which emitted 131 Gg tons of CO₂ (0.1%), and the air transportation subsector, which emitted 13,291 Gg tons of CO₂ (9.2%). The land transportation and railway sectors are the primary contributors to greenhouse gas emissions (Directorat General Climate Change, Ministry Environment and Forestry, Republic Indonesia, 2019).

Based on these considerations, the Government of Indonesia took the initiative to begin construction of the first MRT line to be operational in Jakarta in 2019. The initial project, called MRT Jakarta Phase 1, was designed to facilitate transitioning from private transportation to public transportation. The city of Jakarta was chosen as the location of the MRT project due to its status as the capital of Indonesia, with the highest concentration of private vehicles. This factor contributes to congestion and increased greenhouse gas emissions. Data from the DKI Jakarta Environment Agency show that greenhouse gas (GHG) emissions by the DKI Jakarta reached 38.03 million tons CO₂ in 2010 and increased to 57.6 million tons CO₂ in 2018. This represents a 51.3% increase in GHG emissions during the observed period. The main source of GHG emissions in DKI Jakarta is the combustion of fuel oil for transportation purposes, followed by emissions from the energy sector, especially power plants. The level of greenhouse gas (GHG) emissions in the transportation sector in DKI Jakarta was 7,776,000 tons CO₂ (DKI Jakarta Provincial Environmen Agency, 2019).

One of the primary causes of the elevated GHG emissions in DKI Jakarta can be attributed to the prevalence of motorized vehicles, particularly private cars. According to data from the Central Bureau of Statistics of the DKI Jakarta Province (2019), the number of motorized vehicles in DKI Jakarta in 2019 was 11,839,921. Of these, 8,194,590 units were motorcycles (69.2%), 2,809,989 units were passenger cars (23.7%), 295,370 units were busses (2.5%), and 543,972 units were trucks (4.6%). According to data from the Jakarta Bogor Depok Tangerang Bekasi (Jabodetabek) Urban Transportation Policy Integration (JUTPI) Phase 2, the utilization of public transportation in DKI Jakarta in 2018 remains relatively low at 10%, inclusive of the usage of public busses, TransJakarta, electric rail trains (KRL), taxis, and bajaj (Coordinating Ministry of Economic Affairs collaboration with JICA, 2019). This is because the individual ownership and use of motorized vehicles often results in single-passenger trips, in which the vehicle typically carries only one person. This scenario results in the movement of vehicles rather than the efficient transportation of people and thus contributes to transportation inefficiencies. These inefficiencies manifest in the form of increased time and fuel wastage due to traffic congestion, resulting in increased levels of environmental pollution from exhaust emissions (Anususanto, Priyanto, Munawar, & Wibisono, 2014). It is therefore imperative to reduce the consumption of natural resources, particularly physical space and energy, while also mitigating the associated environmental impacts. Non-motorized modes of transportation, such as walking and bicycles, should be prioritized for short distances. For certain distances that require motorized transportation, it is preferable to utilize modes of transportation with higher capacity and superior energy efficiency, such as high-capacity collective transportation modes, including urban railways, subways, and busses (D’Agosto, 2019). There is potential for CO₂ reduction from shifting the use of private transportation to mass transportation (Hirota, Poot, & Minato, 2003). MRT projects have a good impact on carbon dioxide (CO₂) reduction, mainly in developing cities where the transport demand has increased rapidly. The existing assessment to reduce CO₂ emissions, however, is hardly applicable to urban transportation projects because of limited data (Luathep, Fukuda, Jaensirisak, & Fillone, 2013). Therefore, if MRT development is able to replace the use of private transportation by 2%, it will result in savings of 6.77 Barrels of Oil Equivalent (BOE) per vehicle per year. Similarly, a 0.1% shift from motorcycle users to public transportation would result in savings of 1.17 BOE per vehicle per year (Directorate General of New, Renewable Energy and Energy Conservation, 2020). Furthermore, the substitution of private transportation with MRT will reduce CO₂ emissions (Maimunah & Kaneko, 2016).

It is for this reason that MRT is a mode of transport that has been identified as a potential candidate for inclusion in the CDM project. The emission reductions that can be attributed to this mode of transport can be certified and traded. The impact of MRT Jakarta phase 1 operations on CO₂ emissions has been quantified in several studies in Indonesia. It is important to note, however, that the aforementioned calculations did not employ the CDM methodology proposed by the UNFCCC for obtaining Certified Emission Reductions (CERs). This methodology is more comprehensive than other available methodologies. Nevertheless, it has not been widely employed in the calculation of greenhouse gas (GHG) emission reductions in mass transit projects, primarily because of its complexity and the necessity for extensive primary data. Accordingly, this study presents a simplified version of the CDM formula, adapted to the Indonesian context, for estimating CO₂ emission reductions resulting from the shift from private transportation to MRT. The objective of this approach is to facilitate the accounting of CO₂ emission reductions from MRT projects in Indonesia with a view to carbon certification and trading.

Literatur review

CDM is one of the flexible mechanisms outlined in the Kyoto Protocol that provides emission reduction projects that generate Certified Emission Reductions (CERs), derived from the mitigation activities of CDM Projects (UNFCCC, 2022). The objective is to facilitate the reduction of carbon emissions in developing countries through the implementation of carbon markets, thereby enabling the realization of sustainable development (Zegras, Chen, & Grütter, 2009). The CDM methodology is a methodology that the Intergovernmental Panel on Climate Change (IPCC) uses to estimate CO₂ emission reductions within the transportation sector. It is a sophisticated method that is flexible and depends on data requirements, such as vehicle technology and vehicle type, and can be combined with fuel consumption and average mileage. It is a considerably more rigorous approach than other methodologies of a similar nature (Dharmowijoyo & Tamim, 2010). However, the CDM methodology is challenging to implement, its formula is intricate, the amount of data required is vast, and its costs are considerable. As a result, only 0.3% of the transportation sector is included in the CDM and certified, despite the sector’s contribution of 20% to emissions (Asian Development Bank, 2013). Nevertheless, the CDM methodology offers certain advantages, including the potential to enhance international climate cooperation in climate change mitigation efforts (Wan, Zhang, & Chen, 2024). The United Nations Framework Convention on Climate Change (UNFCCC) has approved more than 250 CDM methodologies for measuring baseline emissions, monitoring emissions, and awarding carbon certificates. While these methodologies are not yet mandatory, they can be adapted in a way that does not undermine the country’s own emission reduction calculations (Michaelowa et al., 2020). Although the CDM methodology is not yet a mandatory requirement of the UNFCCC, particularly for developing countries in measuring CO₂ emissions, the utilization of this method can result in global recognition through the issuance of emission certificates. The existence of comparable emission measurement standards between countries will facilitate the comparison of carbon emissions and facilitate international trade. Nevertheless, the implementation of the CDM methodology in each country is constrained by inherent discrepancies that can be mitigated through adaptations contingent on the completeness of the data requirements and transportation conditions in each country. One potential solution is to simplify the methodology to facilitate its application to CDM projects in each country. Nevertheless, any proposed simplification must be supported by scientific evidence and deemed reasonable.

One of the approved CDM methodologies for the transportation sector is ACM 0016, which was developed for calculating emission reductions from mass transit projects and came into effect in 2010 (Institute for Global Environmental Strategies (IGES), 2011). Nevertheless, this approach presents a significant challenge in practice due to the difficulty of obtaining data to establish an emissions baseline in the absence of mitigation initiatives such as MRT. Consequently, only a limited number of MRT projects can be included in the CDM list (Sharma, Dhyani, & S. Gangopadhyay, 2010). Thus, it is imperative to establish the baseline in a manner that aligns to construct the MRT. For instance, the rationale behind the establishment of MRT Jakarta is to alleviate congestion and curtail CO₂ emissions by encouraging the transition from private vehicle usage to MRT. The baseline for the MRT Jakarta project is therefore the emissions from private vehicle users who have switched to MRT. In order to facilitate implementation and provide greater clarity in determining baseline emissions, emissions from the MRT project, and resulting emission reductions, this study employs simplifications of the ACM 0016 methodology.

Methods

Ethics and consent

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.

Specific location and time of the research

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).

Figure 1. Location map of MRT Jakarta Phase 1.

Source: 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.

Data collection methods

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.

(1)

S = \frac{χ^{2} NP (1 - P)}{d^{2} (N - 1) + χ^{2} P (1 - P)}

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).

Validity and reliability of the instrument

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

Table 1. Validity and reliability test summary.

No	Characteristic	Validity		Reliability
No	Characteristic	Pearson corelation	Significance level (2-Tailed)	Cronbach’s Alpha
1.	Age	0.633	0.001	0.69
2.	Last Education	0.532	0.001
3.	Occupation	0.540	0.001
4.	Income	0.658	0.001
5.	Previous Mode of Transportation	0.851	0.001
6.	Reasons for using MRT	0.561	0.001
7.	Trip Frequency per Week	0.468	0.001
8.	Daily Trip Frequency	0.293	0.039

Analysis method

This study employed a descriptive analytical methodology, whereas the calculation of CO₂ 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 CO₂ 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:

(2)

E R_{y} = B E_{y} - P E_{y}

Where:

ER_y = Emission reduction in year y (tons of CO₂)

BE_y = Baseline emissions in year y (tons CO₂)

PE_y = Emission reduction by MRT Project in year y (tons CO₂)

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:

(3)

B E_{y} = \frac{P_{y}}{P_{SPER}} \sum_{p} (B E_{p, y} \times FE X_{p, y})

Where:

BE_y = Baseline emissions in year y (t CO₂)

BE_p,y = Baseline emissions per surveyed passenger in year y (t CO₂)

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:

(4)

B E_{p, y} = \sum_{i} BT D_{p . iy} \times E F_{pkm, iy} \times 10^{- 6}

Where:

BE_p,y = Baseline emissions per surveyed passenger in year y (t CO₂)

EF_pkm,i,y = Emission factor per passenger kilometer per transport mode in year y (g CO₂/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:

(5)

P E_{y} = DP E_{y} + IP E_{y}

Where:

PE_y = projected emissions in year y (t CO₂)

DPE_y = Project direct emissions in year y (t CO₂)

IPE_y = Indirect emissions of the project in year y (t CO₂)

The direct emissions of the project in year y (t CO₂) are calculated using the following formula:

(6)

{DPE}_{y} = {EC}_{y} \times E F_{E I, y}

Where:

DPE_y = Direct project emissions in year y (t CO₂)

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

EF_El,y = Electricity Grid Emission Factor (kg CO₂/kWh).

Indirect project emissions (IPEy) are calculated using the following formula:

(7)

{IPE}_{y} = \frac{P_{y}}{P_{SPER}} \sum_{P} ({IPE}_{p, y} \times {FEX}_{p, y}) \times 10^{- 6}

Where:

IPE_y = Project indirect emissions in year y (t CO₂)

IPE_p,y = Project indirect emissions per surveyed passenger p in year y (g CO₂)

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.

Results and discussion

Table 2 presents the socioeconomic characteristics of MRT users as revealed by the survey results. As can be seen, the age range of respondents is 15-25 years, representing 32.7% of the total sample. Additionally, users aged 65 years and older constitute 1% of the total sample. Most MRT users have obtained a bachelor’s degree, representing 58% of the total sample. The gender distribution of respondents revealed that 47.1% were male and 52.9% were female. The largest occupational group among respondents was private employees (49% of the total sample. The largest proportion of respondents with incomes above Rp 14,000,000 was 20.8%.

Table 2. Socioeconomic characteristics of MRT user respondents.

No	Socioeconomic	Characteristic	Total	Percentage
1.	Age	15-25 years	157	32.7%
		≥25-35 years	145	30.2%
		≥35-45 years	95	19.8%
		≥45-50 years	61	12.7%
		≥55-65 years	17	3.5%
		≥65 years old	5	1.0%
2.	Last Education	Elementary School	3	0.6%
		Junior high school	3	1%
		Senior high school	70	15%
		Diploma	47	10%
		Bachelor	279	58%
		Postgraduate	78	16%
3.	Gender	Male	226	47.1%
3.	Gender	Female	254	52.9%
4.	Occupation	Civil servants, military, police	26	5.4%
		SOE Employee	10	2.1%
		Private Employee	235	49.0%
		Self-employed	49	10.2%
		Student	114	23.8%
		Housewife	25	5.2%
		Retired	4	0.8%
		Others	17	3.5%
5.	Income	<Rp 1,000,000	43	9.0%
		≥Rp 1,000,000 – Rp 3,000,000	65	13.5%
		≥Rp 3,000,000 – Rp 5,000,000	81	16.9%
		≥Rp 5,000,000 – Rp 7,000,000	66	13.8%
		≥Rp 7,000,000 – Rp 9,000,000	56	11.7%
		≥Rp 9,000,000 – Rp 12,000,000	36	7.5%
		≥Rp 12,000,000 – Rp 14,000,000	33	6.9%
		≥Rp 14,000,000	100	20.8%

Table 3 shows the results of the MRT usage survey as follows: the most used transportation before switching to MRT was TransJakarta BRT at 32.2%, private cars at 25.21%, and motorcycles at 28.54%. The total number of private transportation users was 258 of 480 respondents, representing 53.75% of the sample. Regarding travel purpose, 52.8% of respondents traveled for work, 24.2% for sightseeing, and 8.85% for business. Based on the daily travel frequency is presented, 83.7% traveled by MRT to go and return, 11.3% traveled, and 5% traveled back.

Table 3. Choice of answers about MRT usage.

No	MRT usage	Choice of answer	Total	Percentage
1.	Previous Mode of Transportation	BRT TransJakarta	167	34.8%
		Regular Bus (Large bus public transportation)	13	2.7%
		Metromini (Medium bus public transportation)	1	0.2%
		Mikrolet (City Car public transportation)	4	0.8%
		Taxi	22	4.6%
		Bajaj	1	0.2%
		Private Car	121	25.21%
		Motorcycle	137	28.54%
		Bicycle	1	0.2%
		Walking	10	2.1%
2.	Reasons for using MRT	Work	253	52.8%
		Business	42	8.8%
		Shopping	16	3.3%
		School/campus	33	6.9%
		Traveling	116	24.2%
		Social visit/hospitalization	7	1.5%
		Other	12	2.5%
3.	Trip Frequency Per Week	Every day	59	12.3%
		Every Weekday	156	32.5%
		Every Weekend/Holiday	10	2.1%
		At certain times only/as needed	255	53.1%
4.	Daily Trip Frequency	Just go	54	11.3%
		Return only	24	5.0%
		Go and Return	401	83.7%

The profile of MRT Jakarta Phase 1 users indicates that the MRT is a mode of transportation that has been able to attract individuals belonging to the upper-middle-class economy, as evidenced by the responses of respondents who were predominantly those with high levels of education and income. Most respondents use MRT for their daily commutes, with the primary objective being work-related.

Estimated CO₂ emissions reduction after MRT Jakarta Phase 1

This study’s estimation of CO₂ 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 (CO₂) 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 CO₂ emission reductions calculated in this study are exclusively associated with emissions reductions along roads parallel to the MRT corridor.
2. The analysis does not incorporate leakage from public conveyance or other road segments. Leakage in public transportation is defined as a reduction in passenger occupancy (load factor) resulting from migrating other public-transport users to the MRT system. This study did not include surveys to assess the dynamic load factor of other public transportation modes, such as Bus TransJakarta, micropet, metrodin, and other public vehicles operating along routes parallel to the MRT. Moreover, speed and travel time surveys were not conducted on alternative road sections running parallel to the MRT corridor.
3. The passenger expansion factor was assumed to be 1 (one) due to the unfeasibility of sampling at the station using the systematic method of calculating the line length. This is because the station-waiting time is only 5 min, which is insufficient for the application of the proposed methodology. Sampling of passengers was conducted on the vehicles themselves using incidental sampling techniques.

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

Table 4. Simplified CDM ACM 0016 for CO₂ Emission Reduction Calculation.

Variable Name	Originally	Become (Simplicity)	Description
Emission Reduction in Year y	$E R_{y} = B E_{y} - P E_{y} + L E_{y}$	$E R_{y} = B E_{y} - P E_{y}$	Modifications have been made, except those that were disclosed.
Baseline Emissions for Year y	$B E_{y} = \frac{P_{y}}{P_{SPER}} \sum_{p} (B E_{p, y} \times FE X_{p, y})$	$B E_{y} = \frac{P_{y}}{P_{SPER}} \sum_{p} B E_{p, y}$	The formula was modified as a result of the expansion factor being assumed as 1.
Baseline Emissions per Passenger	$B E_{p, y} = \sum_{i} BT D_{p . iy} \times E F_{pkm, iy} \times 10^{- 6}$	$B E_{p, y} = \sum_{i} BT D_{p . iy} \times E F_{pkm, iy} \times 10^{- 6}$	Remain
Project Emissions	$P E_{y} = DP E_{y} + IP E_{y}$	$P E_{y} = DP E_{y}$	The formula was modified to exclude indirect emissions.

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.

Calculating baseline CO₂ emissions

In calculating the baseline CO₂ emissions, the following factors were considered: the CO₂ 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.

Table 5. Baseline CO₂ emissions from survey on private transport usage per day.

The type of Transportation	Number of Passengers	Baseline mileage per passenger (Km/Passenger)	Emission factor per passenger kilometer per transport mode in year y (g CO₂)	Baseline Emissions of Surveyed Passengers (g CO₂) per day
	P_sper	BTD_p,i,y	EF_pkm,i,y	BE_p,y
	(1)	(2)	(3)	(4) = (1) X (2) (3)
Car Online	71	17.8	24.322	30,678.4
Ojek Online	112	17.2	48.645	93,967.7
Private Cars	50	19.7	24.322	23,919.9
Motorcycles	25	17.3	48.645	21,059.6
Jumlah	258			169,625.6

The calculation in Table 5 assumes that private transportation users use premium fuel, which has a local emission factor of 72,967 kg CO₂/TJ (72,967 g CO₂/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 CO₂ per day or 169.8 kg CO₂ per day for one trip. If two round trips are completed, the baseline emissions for the MRT corridor are 339.3 kg CO₂ 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 CO₂ or 16,706.7 tons CO₂ (equivalent to 16,706,700 kg CO₂) as shown in Table 6.

Table 6. The baseline emissions of private transport users in 2019.

Baseline emissions of surveyed passengers (kg CO₂/day)	Baseline emissions (BE_y) All private transport users in the survey years
BE_p,y	BE_y (kg CO₂)	BE_y (ton CO₂)
(5)	(6)	(7)
339.3	16,706,684.4	16,706.7

Estimation CO₂ emissions of MRT project

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 CO₂ per MWh (ESDM, 2019). Based on these calculations, the direct emissions of the MRT project in 2019 were 13,974.03 tons of CO₂. This is shown in Table 7.

Table 7. Baseline and MRT project emissions in 2019.

Direct emissions (DPE_y)
Electricity consumption (for MRT traction only) year y (MWh)	Electricity grid emission factor (tons CO₂/MWh).	Direct emissions of the project in year y (t CO₂)
EC_y	EF_El,y	DPE_y
(8)	(9)	(10) = (8)*(9)
16,062.1	0.87	13,974.03

Calculating CO₂ emission reduction in 2019

The reduction in CO₂ 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 CO₂ are subtracted from the MRT project emissions of 13,974.03 tons of CO₂, resulting in a reduction value of 2,732.7 tons of CO₂ in 2019. Thus, the reduction in CO₂ emissions resulting from the shift from private transportation to MRT averaged 227.7 tons of CO₂ per month, with a daily average of 9.9 tons of CO₂ in 2019. This is shown in Table 8.

Table 8. CO₂ emission reduction from the MRT project for 2019.

Baseline (BE_y)		MRT project emissions	Emission reduction (tons/year)
BE_y (kg CO₂)	BE_y (ton CO₂)	PE_y = DPE_y	ER = Bey-Pey
(6)	(7)	(10)	(11)
16,706,684.4	16,706.7	13,974.03	2,732.7

Estimation of CO₂ emission reduction in 2023

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.
b. The passenger emission factor using the premium was 72,600 kg CO₂/TJ (Ministry of Energy and Mineral Resources, 2019).
c. MRT daily average electricity consumption based on the analysis of secondary data obtained from MRT.
d. The electricity network emission factor is 0.87 tons CO₂
e. The passenger expansion factor was assumed to be 1 because sampling was not carried out at the station node but was carried out onboard, did not use queues, and was not categorized in clusters.
f. Leaked emissions were not considered because the shifting included in the calculation only included shifting private transportation to MRT and not shifting from other public transportation modes.
g. Indirect error is not considered because the travel pattern from origin to destination generally still uses fossil fuel transportation.

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 CO₂ 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 CO₂ 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.
b. The emission factor for premium car users is 72,600 kg CO₂/TJ (Directorate General of New Renewable Energy and Energy Conservation, 2020).
c. The daily average MRT electricity consumption was based on the results of secondary data analysis obtained from MRT.
d. The emission factor of the power grid is 0.87 tons of CO₂.
e. Passenger growth factor was assumed to be 1 because sampling was not performed at station nodes but on trains without using queues and not categorized in clusters.
f. Emissions leaked due to the transfer of other public transportation to the MRT were not included in the analysis, as it only included transfers from private vehicles to the MRT and not transfers from other public vehicles.

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 CO₂ per day.
2. The baseline emissions per passenger is 1.3 kg CO₂, i.e. 339.3 kg CO₂ divided by the number of respondents.
3. The baseline calculation for a given year is obtained by multiplying the baseline per passenger by the number of passengers in the observed year (in 2023).
4. The next step is to calculate the emissions generated by the MRT project.

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 CO₂ as shown in Table 9.

Table 9. Baseline CO₂ emissions from the 2023 MRT project.

Baseline emissions of surveyed passengers (kg CO₂/day)	Baseline (BEy)
BE_p,y	BE_y (kg CO₂)	BE_y (ton CO₂)
(5)	(6)	(7)
339.3	16,976,857	16,976.9

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 CO₂, 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 CO₂ 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.

Table 10. MRT project emissions for 2023.

Electricity consumption (for MRT traction only) year y (MWh)	Emission factor of electricity grid (tons CO₂/MWh).	Direct emissions of the project in year y (t CO₂)
EC_y	EF_El,y	DPE_y
(8)	(9)	(10) = (8)*(9)
12,567	0.87	10,932.99

Table 11. CO₂ Emission Reduction in 2023.

Baseline (BE_y)		MRT project emissions	Emission reduction (tons/year)
BE_y (kg CO₂)	BE_y (ton CO₂)	PE_y = DPE_y	ER = Bey-Pey
(6)	(7)	(10)	(11)
16,976,857	16,976.9	10,932.99	6,043.9

The results of this study indicate that 53.75% of MRT Jakarta Phase 1 users transitioned from private transportation, leading to a reduction in CO₂ emissions of 2,732.7 tons in 2019 and 6,043.9 tons in 2023. The value of CO₂ 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 CO₂ (DKI Jakarta Provincial Environment Office, 2019). Ini 2022, the implementation of MRT Jakarta Phase 1 resulted in a reduction of 2,104 tonnes of CO₂ 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 CO₂ 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, CO₂ 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 CO₂ 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 CO₂ reductions. Conversely, if the number of MRT users decreases, the MRT project may increase CO₂ 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 CO₂ 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.

Strengths and limitations

The strength of this research is that the calculation of CO₂ 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 CO₂ 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.

Conclusion

The results of the primary survey conducted in this study indicate that 53.75% of MRT users have transitioned from private transportation. Moreover, the simplified CDM ACM 0016 method yielded a CO₂ emission reduction calculation of 2,732.7 tons of CO₂ in 2019 and 6,043.9 tons of CO₂ in 2023. It should be noted that the reductions in CO₂ emissions in 2020, 2021, and 2022 were not calculated in this study due to the ongoing impact of the Corona Virus Disease 19 (Covid-19) pandemic, which has resulted in significant restrictions on passenger capacity in vehicle transportation, with reductions ranging from 50% to 75%.

This study demonstrates that the CDM method for measuring CO₂ emission reductions can be applied in Indonesia. However, the formula must be simplified to align with the transportation sector’s characteristics and data availability to ensure its effectiveness. The CDM ACM 006 formula streamlining is anticipated to be implemented in Indonesia. This would facilitate the certification of CO₂ emission reductions through mitigation actions through MRT development by the UNFCCC.

It is crucial to cultivate greater interest among private transportation users in opting for MRT, thereby augmenting the number of MRT passengers. This can be achieved by implementing strategies such as offering incentives to private transportation users to transition to MRT, expanding the operational scope of MRT, enhancing integration facilities, improving accessibility to MRT stations, and developing a feeder ecosystem around stations. Furthermore, to augment the quantity of emission reductions generated from MRT operations, it is imperative to transition the utilization of energy sources for MRT operations toward renewable energy sources, such as solar power, hydropower, and nuclear power. Low emission factors characterize these energy sources and have considerable potential to curtail future greenhouse gas emissions.

Ethics and consent

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.

Data availability

Underlying data

1. Figshare: Primary Survey Data in the MRT https://doi.org/10.6084/m9.figshare.26824318.v3 (Nurdjanah, Primary_Survey_Data_in_the_MRT, 2024a)

This project contains following dataset:

• Primary Survey Data in the MRT.xls.

2. Descriptive of Respondent’s Answer: https://doi.org/10.6084/m9.figshare.26826532 (Nurdjanah, Descriptive of Respondent’s Answer, 2024b)

This project contains following dataset:

• Descriptive of MRT Respondent Answer,xls

Data are avalilable under the terms of the Creative Commons Attribution 4.0 Internasional license (CC-BY 4.0)

Extended data

Figshare: Survey form of mrt users: https://doi.org/10.6084/m9.figshare.26889658.v1 (Nurdjanah, SURVEY_FORM_OF_MRT_USER, 2024c)

This project contains following dataset:

• SURVEY FORM OF MRT USERS.PDF

Data are avalilable under the terms of the Creative Commons Attribution 4.0 Internasional license (CC-BY 4.0)

References

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Author details Author details

¹ School of Environmental Science, Universitas Indonesia, Jakarta Pusat, West Java, 10430, Indonesia
² Transportation Policy Agency, Ministry of Transportation, Jakarta Pusat, DKI Jakarta, 10110, Indonesia

Nunuj Nurdjanah
Roles: Conceptualization, Data Curation, Formal Analysis, Funding Acquisition, Investigation, Methodology, Project Administration, Resources, Software, Supervision, Validation, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing

Tri Edhi Budhi Soesilo
Roles: Conceptualization, Formal Analysis, Methodology, Project Administration, Resources, Supervision, Writing – Review & Editing

Kosuke Mizuno
Roles: Conceptualization, Formal Analysis, Methodology, Project Administration, Resources, Supervision, Writing – Review & Editing

Raldi Hendro Koestoer
Roles: Conceptualization, Formal Analysis, Methodology, Project Administration, Resources, Supervision, Writing – Review & Editing

Competing interests

No competing interests were disclosed.

Grant information

The author(s) declared that no grants were involved in supporting this work.

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Nurdjanah N, Soesilo TEB, Mizuno K and Koestoer RH. Simplification of clean development mechanism to measure CO₂ emission reductions from shifting private transportation to mass rapid transit: a case study of MRT Jakarta Phase 1 [version 1; peer review: 1 approved with reservations]. F1000Research 2024, 13:1069 (https://doi.org/10.12688/f1000research.155406.1)

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Reviewer Report 17 Oct 2024

Qing Yu, Peking University Shenzhen Graduate School, Shenzhen, Guangdong, China

Approved with Reservations

https://doi.org/10.5256/f1000research.170581.r331896

This paper is well organized and easy to understand. Through its research on the Jakarta MRT Phase 1 project, the article proposes a simplified CDM model and provides a detailed carbon emission estimation, offering significant policy implications and practical value. ... Continue reading

This paper is well organized and easy to understand. Through its research on the Jakarta MRT Phase 1 project, the article proposes a simplified CDM model and provides a detailed carbon emission estimation, offering significant policy implications and practical value. However, the paper can be further improved in the following areas:
1. Figure 1 is too blurry: The clarity needs to be improved.
2. Data details: The paper only provides an overview of the survey information but does not explain what specific data was collected. Does it include passenger travel OD information for each trip? If not so, what kind of travel information is included? This is crucial for the carbon emission calculations, and I suggest adding more details in the data description section.
3. Simplified vs. original CDM ACM 0016 method: The paper mentions that a simplified version of the CDM ACM 0016 method is used to calculate the carbon emissions and reductions for the MRT project. However, there is no error analysis or quantitative comparison between the simplified and original versions. While the simplification makes the CDM ACM 0016 method more practical for application, there is no sensitivity analysis or uncertainty assessment to evaluate the accuracy of the simplified results. This is a notable shortcoming of the paper.
4. Comparison between 2019 and 2023 emissions: Why does the paper compare emissions for 2019 and 2023 specifically? Why were these two years chosen, and how are future scenarios considered? The 2023 data is not based on direct observations but is predicted and assumed based on 2019 data. The paper assumes that future travel patterns and passenger growth rates will be similar to 2019. Is there any real data available for some level of validation of these predictions?
5. Policy recommendations: I suggest adding more policy recommendations based on the results to enhance the practical reference value of this study for urban carbon reduction strategies in transportation policy.

Is the work clearly and accurately presented and does it cite the current literature?

Yes
Is the study design appropriate and is the work technically sound?

Yes
Are sufficient details of methods and analysis provided to allow replication by others?

Yes
If applicable, is the statistical analysis and its interpretation appropriate?

Partly
Are all the source data underlying the results available to ensure full reproducibility?

Partly
Are the conclusions drawn adequately supported by the results?

Partly

Competing Interests: No competing interests were disclosed.

Reviewer Expertise: Transportation data science, Transportation energy and emissions, Electric vehicles, Public transportation, Urban planning & mobility, Agent-based simulation

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.

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17 Oct 2024 | for Version 1

Qing Yu, Peking University Shenzhen Graduate School, Shenzhen, Guangdong, China

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Approved With Reservations

This paper is well organized and easy to understand. Through its research on the Jakarta MRT Phase 1 project, the article proposes a simplified CDM model and provides a detailed carbon emission estimation, offering significant policy implications and practical value. However, the paper can be further improved in the following areas:
1. Figure 1 is too blurry: The clarity needs to be improved.
2. Data details: The paper only provides an overview of the survey information but does not explain what specific data was collected. Does it include passenger travel OD information for each trip? If not so, what kind of travel information is included? This is crucial for the carbon emission calculations, and I suggest adding more details in the data description section.
3. Simplified vs. original CDM ACM 0016 method: The paper mentions that a simplified version of the CDM ACM 0016 method is used to calculate the carbon emissions and reductions for the MRT project. However, there is no error analysis or quantitative comparison between the simplified and original versions. While the simplification makes the CDM ACM 0016 method more practical for application, there is no sensitivity analysis or uncertainty assessment to evaluate the accuracy of the simplified results. This is a notable shortcoming of the paper.
4. Comparison between 2019 and 2023 emissions: Why does the paper compare emissions for 2019 and 2023 specifically? Why were these two years chosen, and how are future scenarios considered? The 2023 data is not based on direct observations but is predicted and assumed based on 2019 data. The paper assumes that future travel patterns and passenger growth rates will be similar to 2019. Is there any real data available for some level of validation of these predictions?
5. Policy recommendations: I suggest adding more policy recommendations based on the results to enhance the practical reference value of this study for urban carbon reduction strategies in transportation policy.

Is the work clearly and accurately presented and does it cite the current literature?

Yes
Is the study design appropriate and is the work technically sound?

Yes
Are sufficient details of methods and analysis provided to allow replication by others?

Yes
If applicable, is the statistical analysis and its interpretation appropriate?

Partly
Are all the source data underlying the results available to ensure full reproducibility?

Partly
Are the conclusions drawn adequately supported by the results?

Partly

Competing Interests

No competing interests were disclosed.

Reviewer Expertise

Transportation data science, Transportation energy and emissions, Electric vehicles, Public transportation, Urban planning & mobility, Agent-based simulation

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.

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Simplification of clean development mechanism to measure CO2 emission reductions from shifting private transportation to mass rapid transit: a case study of MRT Jakarta Phase 1

Abstract

Background

Methods

Result

Conclusion and implications

Keywords

Introduction

Literatur review

Methods

Ethics and consent

Specific location and time of the research

Figure 1. Location map of MRT Jakarta Phase 1.

Data collection methods

(1)

Validity and reliability of the instrument

Table 1. Validity and reliability test summary.

Analysis method

(2)

(3)

(4)

(5)

(6)

(7)

Results and discussion

Table 2. Socioeconomic characteristics of MRT user respondents.

Table 3. Choice of answers about MRT usage.

Estimated CO2 emissions reduction after MRT Jakarta Phase 1

Table 4. Simplified CDM ACM 0016 for CO2 Emission Reduction Calculation.

Calculating baseline CO2 emissions

Table 5. Baseline CO2 emissions from survey on private transport usage per day.

Table 6. The baseline emissions of private transport users in 2019.

Estimation CO2 emissions of MRT project

Table 7. Baseline and MRT project emissions in 2019.

Calculating CO2 emission reduction in 2019

Table 8. CO2 emission reduction from the MRT project for 2019.

Estimation of CO2 emission reduction in 2023

Table 9. Baseline CO2 emissions from the 2023 MRT project.

Table 10. MRT project emissions for 2023.

Table 11. CO2 Emission Reduction in 2023.

Strengths and limitations

Conclusion

Ethics and consent

Data availability

Underlying data

Extended data

References

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Simplification of clean development mechanism to measure CO₂ emission reductions from shifting private transportation to mass rapid transit: a case study of MRT Jakarta Phase 1

Estimated CO₂ emissions reduction after MRT Jakarta Phase 1

Table 4. Simplified CDM ACM 0016 for CO₂ Emission Reduction Calculation.

Calculating baseline CO₂ emissions

Table 5. Baseline CO₂ emissions from survey on private transport usage per day.

Estimation CO₂ emissions of MRT project

Calculating CO₂ emission reduction in 2019

Table 8. CO₂ emission reduction from the MRT project for 2019.

Estimation of CO₂ emission reduction in 2023

Table 9. Baseline CO₂ emissions from the 2023 MRT project.

Table 11. CO₂ Emission Reduction in 2023.