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Journal Of Economics, Technology, and Business (JETBIS)

Volume 3, Number 2 February 2024

temperatures are currently estimated to be about 1.20°C above the average temperature in pre-

industrial times (Organization, 2022) and 2022 will be the sixth warmest year since 1880

(National Oceanic and Atmospheric Administration, 2023). Projections by 2100, even if

emissions are significantly reduced and global warming is kept to less than 2°C, sea levels

could still rise by 30 to 60 centimeters (Affairs, 2022).

One of the main contributors to climate change is greenhouse gas emissions resulting

from various human activities. Four gaseous components, namely carbon dioxide (CO2),

Methane (CH4), Nitrous oxide (N2O), and Fluorinated gases (F-gases), form greenhouse gas

emissions at the global level. CO2 gas is mainly produced from the use of fossil fuels, while

waste incineration, energy use, and biomass burning produce methane gas. In addition,

As shown in Figure 1, CO2 is the largest component in the formation of greenhouse gas

emissions with a share of 65% sourced from fossil fuels and industrial processes and 11% from

forest burning processes, so that overall it contributes 76% of the amount of greenhouse gases

created. Therefore, the main focus in efforts to reduce global climate change is to encourage

the reduction of CO2 emissions.

Indonesia's greenhouse gas emissions totaled 950 MtCO2eq and were among the largest

in the world in 2018 (Gütschow et al., 2016).

According to data from the 2021 Greenhouse Gas Inventory (GHG) and Monitoring,

Reporting, Verification (MPV) report prepared by the Ministry of Environment and Forestry

as shown in Figure 2 above, greenhouse gas contributors are dominated by four sectors, namely

energy, Industrial Process and Product Use (IPPU), agriculture, and waste. The energy sector

as the largest contributor of more than 60% mainly comes from the energy and transportation

industry which still depends on the use of fossil energy as the main fuel. The consequence of

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emissions by 2060. Indonesia's seriousness in mitigating climate change is the basis for the

government in formulating Indonesia's NDC (Nationally Determined Contributions). The

Indonesian government has integrated mitigation and adaptation elements to achieve the NDC

goal of reducing 31.89% of carbon emissions nationally and 43.20% with international

assistance by 2030 (Coordinating Ministry for Economic Affairs of the Republic of Indonesia,

2022). The goal is to achieve a low-emission and climate-resilient Indonesia in the future.

The transportation sector consumes 428.61 million BOE (Barrel Oil Equivalent) or about

36.15% of the final energy consumption of 1,185.56 million BOE in 2022 (Ministry of Energy

and Mineral Resources, 2023). The high energy use in the transportation sector is reflected in

the number of vehicles in Indonesia that are still dominated by conventional vehicles (ICE),

two-wheeled and three-wheeled electric motorcycles and 600 thousand units of electric cars

and electric buses by 2030 (Junida, 2023). It is expected that the use of 2 million electric cars

by 2030 will reduce fuel imports by 3 million kiloliters and reduce CO2 emissions by 6.42

million tons/year.

Three types of electric vehicles exist in many countries including Indonesia. First, a

Hybrid Electric Vehicle (HEV) is a vehicle that uses an internal combustion engine (ICE) but

has electric power as a backup. Second, Plug Hybrid Electric Vehicle (PHEV) is a vehicle that

combines two types of engines at once, namely internal combustion engines and batteries.

Third, Battery Electric Vehicle (BEV) is a vehicle that entirely uses electricity as an energy

source (Hendarmin et al., 2023).

One of the barriers to the adoption of electric vehicles in Indonesia is the high purchase

incentives to consumers. This policy provides BBN KB incentives for people who are

interested in buying battery-based electric vehicles. In total, this incentive reduces about 12.5%

of the selling price of battery-based electric vehicles. With these incentives, consumers will be

more interested in owning battery-based electric vehicles in Indonesia. Thus, in theory, the

demand curve will shift to the right. This leads to an increase in the quantity of demand for

battery-based electric vehicles. However, if people still do not have environmental awareness

and there is no policy to limit the use of conventional vehicles, the demand for battery-based

electric vehicles will be inelastic.

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consumers, one of which is related to purchase costs and operating costs (Gnann et al., 2018).

The cost groups of purchase price and operating costs are captured by the concept of total cost

of ownership (Danielis et al., 2018). Total Cost Ownership has been defined as a purchasing

tool and philosophy, which aims to understand the true cost of purchasing a particular item

such as a car (Ellram, 1995). Total Cost Ownership is consumer-oriented which includes all

costs borne by vehicle users such as purchase price, fuel consumption, vehicle taxes,

maintenance, maintenance, repairs, depreciation, and so on (Danielis et al., 2019).

energy, namely electricity. The battery-based electric motor vehicle (KBLBB) policy is part of

the government's long-term efforts to reduce greenhouse gas (GHG) emissions. The battery-

electric cars in Indonesia, is still very low. The number of battery-based electric car

achievements is still far from the target set by the government of 400 thousand units by 2025

or a new battery-based electric car population of 3.95%. Therefore, the success of this policy

needs to be measured by analyzing the adoption of battery-based electric vehicles in Indonesia.

Research related to government policies and interventions in increasing motor vehicle

ownership has been conducted. Such as research conducted (Mannberg et al., 2014) on the

effect of congestion tax in Sweden on the purchase of tax-free ethanol cars found that the

imposition of congestion tax has a significant impact on the sale of ethanol-fueled cars in the

city of Stockholm. The positive impact can be seen from the increase in sales when ethanol car

purchases are incentivized by congestion tax exemptions. Another study also found that the

impact of fiscal incentives in the form of tonnage tax cuts and green vehicle acquisition taxes

incentive policies in the form of tax exemptions and reductions. In addition, this research can

also be used as a reference for policymakers related to government strategic programs.

In this study, the unit of analysis used is the type of vehicle in the form of conventional

or electric at the provincial level with secondary data obtained from various sources. The main

dependent variable is data on the number of ownership of conventional and electric motorized

vehicles from all provinces in Indonesia. The ownership data is obtained from the Korlantas

Source: processed by the author (2023)

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motor vehicle ownership. motor vehicle ownership is data on motor vehicles that have been

registered and determined by the Korlantas Polri per province, per month, and year. The use of

ownership/registration data as a dependent variable is in line with research conducted by

(Tláskalová, 2021) and (Wee et al., 2018). The use of registration is because this research

focuses on taxation, especially local taxes at the regional level. The concept of local tax is local

base so it follows where the vehicle is registered. Because it could be that the purchase is made

in region A but the use is in region B. Furthermore, 2019 for data before the policy, and 2020

to 2022 is data after the policy is implemented. Then the independent variables consist of

variables that are used as proxies for priority interventions in measuring ownership of battery-

based electric vehicles. Where the main independent variable is the dummy treatment of

per capita, household expenditure per capita, fuel prices, inflation, unemployment rates, and

consumer awareness are used as independent control variables.

Based on Table 2, the development of vehicle ownership can be seen from the market

share of the number of 4-wheeled motorized vehicle ownerships. The market share of vehicle

ownership (lnkepilikan) with the lowest value of 0 and the highest value of 4.46 was in DKI

Jakarta Province in 2019. The average market share value of vehicle ownership is 0.70.

Furthermore, in terms of control variables, we can see the condition of the number of charging

stations (lnspklu), PDRB per capita (lnpdrb_kap), household expenditure per capita

(lnpengel_kap), fuel price (lnbm), inflation (inflation), unemployment rate (lnunemploy_rate),

and consumer knowledge/concern (cons_awar).

As Figure 5 shows, before the existence of the BBN KB incentive policy, the number of vehicle

ownership between battery-based electric vehicles and conventional vehicles had the same trend from

2019 to 2020. Then, there was a significant increase in the number of vehicle ownership in Indonesia

from 2020 to 2021, where 2020 was the time when the policy was implemented. The number of vehicle

owners for vehicle types that receive BBN KB incentives shows a better growth trend and tends to

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increase. This is in line with demand theory (Pindyck & Rubinfeld, 2014) that the quantity of demand

for a good will change if there are factors that influence it, such as the provision of BBN KB

subsidies/incentives. Meanwhile, vehicle types that do not receive incentives experience stagnant

growth. In this case, for the unit of analysis that does not receive incentives, the trend in the number of

vehicle ownership does not seem to have increased either before the policy or after the policy.

Furthermore, a robustness test is also carried out to further ensure that the assumptions of the

analysis. Furthermore, the variable that has a random value is regressed on the OLS interaction model.

Based on Table 3, the results of the robustness test show that the treatime2 interaction

variable has analysis results that have no significant effect. Thus it can be concluded that the

model can be applied to estimate the effect of the BBN KB incentive policy on increasing

ownership of battery-based electric motorized vehicles. This means that the results of empirical

panel data processing and testing can produce fairly strong policy analysis findings.

This research initially wanted to see the causal inference between the provision of the

BBN KB incentive policy and the level of ownership of battery-based electric vehicles in

inference relationship. Therefore, this study aims to identify the effect of policies on the growth

Tláskalová, A. (2021). The Impact of Incentives on Electric Vehicle Sales in the European

https://dspace.cuni.cz/bitstream/handle/20.500.11956/150215/120398618.pdf?sequence

=1

Wee, S., Coffman, M., & La Croix, S. (2018). Do electric vehicle incentives matter? Evidence

from the 50 US states. Research Policy, 47(9), 1601–1610.

Xue, C., Zhou, H., Wu, Q., Wu, X., & Xu, X. (2021). Impact of incentive policies and other

socio-economic factors on electric vehicle market share: A panel data analysis from the