The Future of Carbon Dioxide Emission in Indonesia: Does Deforestation Matter?

Indonesia is a country with one of the highest rates of deforestation in the world. Ten million hectares of main forests in Indonesia had disappeared in the past two decades and 71% of trees in natural forests in Indonesia have been cut during 2013 to 2021. It is estimated that 10% of the CO2 emissions triggered by human actions are from deforestation. Since high CO2 emissions is a negative externality, it will become a burden to society if this problem exists. One of the possibilities to reduce the CO2 emissions problem is through the protection of the forests. Since the presence of forests is important to absorb the carbon produced by human activities, this study tries to find the fact whether there is an impact of deforestation on CO2 emissions in Indonesia by using econometrics modelling. This study applies Dynamic Ordinary Least Square (OLS) method to examine the impact of deforestation on CO2 emissions. Two models are set with two different data as the representation of deforestation variable. First data is forest area coverage as the independent variable, and the second data is the annual deforestation rate as the independent variable, this study uses CO2 emissions as the dependent variable. The results of both models confirm solid evidence that deforestation is contributing to higher level of CO2 emissions in Indonesia. In term of policy implications, some suggestions for the Indonesian government are (1) enforces reforestation as it is the best way to prevent deforestation, (2) applies more strict punishment for illegal logging, and (3) promotes inclusive and sustainable forest management.


Introduction
Indonesia has abundant natural resources.The natural resources are one of the main boosters of Indonesia's economy.Indonesia is the world's rank three of the largest region of rainforest after the Amazon and Africa's Congo Basin.Indonesia was the world's largest raw log exporter as well as the world's major plywood producer in the late of 1980's.Timber contributes in the second largest share to the Indonesian economy after oil and gas.Without reforestation, the systematic cutting of trees resulted in declining of forest cover area.Forest area in Indonesia was at 49.07 % in 2020, declined from 53.1% IOP Publishing doi:10.1088/1755-1315/1248/1/012033 2 in 2010 [1].However, high dependency on the forest creates a big issue.The consequence is that deforestation has become a main problem to Indonesia.
In Indonesia, the deforestation in the period of year 1996 -2000 was 3.51 million hectares.Deforestation decreased to 1.09 million hectares in the period of year between 2014 -2015, and 470 thousand hectares in 2018-2019.The deforestation rate in the period 2019 -2020 decreased by 75 percent to 115 thousand hectares.[2].The policies and programs implementation by Indonesian Government successfully reduce deforestation rate with a new all-time low record in 2020, the lowest record.However, in 2020 Indonesia still lost 115,459 hectares of forest area, an area the size of Los Angeles [3].Deforestation is the trigger of around 10% of global warming [4].
Homo sapiens, or humans, are the one who are hold responsible to the damage of the environment [5].Their economic actions, for example through cutting down trees massively for short run profit, cause the degradation of the environment.In 2021, CO2 emissions for Indonesia was 602.6 million tonnes.From 1972 to 2021, CO2 emissions of Indonesia worsened significantly from 37.4 to 602.6 million tonnes, with a maximum annual rate of 36.40% in 1977 and then decreased to 1.91% in 2021 [6].
There is almost no previous study about the impact of deforestation on CO2 using econometrics methods.A study using panel data method was conducted in West African Economic and Monetary Union (WAEMU) Countries by [7].Their study conclude that deforestation causes higher CO2 emissions in the region.Since there is only limited previous studies, the aim of this study was to investigate the impact of deforestation on Carbon Dioxide Emission in Indonesia.To the best of our knowledge, the investigation about the impact of deforestation on Carbon Dioxide Emission in Indonesia using econometrics methods do not exist.So, it is vital to investigate empirically by using econometrics how deforestation may influence Carbon Dioxide Emission level in Indonesia.To sum up, this study offers significant impact to recent literature about the impact of deforestation on Carbon Dioxide Emission in Indonesia.This study suggests new empirical method using Dynamic Ordinary Least Square to fill the gap in the literature.
The paper is arranged as follow: Section 1 is an introduction that briefly discusses at the background of the connection between deforestation and Carbon Dioxide Emission.Section 2 describes the empirical method.Section 3 exhibits the discussion of the results and finally the Section 4 is the conclusion.

Methodology
This study uses Carbon Dioxide Emission as the dependent variable and the rate of losing forest area, or deforestation, is the independent variable.In addition, this study also uses forest area coverage as the proxy of deforestation.So, there are two models in this study.This study also employs three other independent variables in the model, i.e., income per capita, population and inflation that are likely have impact on Carbon Dioxide Emission.This study employs econometric techniques, i.e., Dynamic Ordinary Least Square (DOLS) to mainly examine the impact of deforestation on Carbon Dioxide Emission in Indonesia.The DOLS model is used in this study because the DOLS dynamic long run among the cointegrating variables.In addition, DOLS offers a simple yet efficient method to calculate approximately the coefficients of a cointegrating variables.The first model is stated as follow: Next, the second model as follow: Where, Log represents the logarithm function, βo, β1, β2, β3 are the coefficients for the first DOLS model, et is the error term of the first model, α0, α1, α2, α3, α4 are the parameters for the second DOLS model, εt is the error term of the second model and t represents the time.The DOLS models must be cointegrated before they can be used for the analysis.The cointegrated variables mean that all variables in the models have long run relationship.In this study, Hansen Parameter Instability test is used to check the cointegration.

Results and Discussion
The first step of DOLS model is to check whether the models have cointegration or not.The Hansen Parameter Instability tests, as seen in Table 1, suggest that cointegration is present in both models.Both models have probability more than 0.2, in which the models reject non cointegrating variables.Therefore, we confirm that all variables in the models have long run relationship and DOLS Models are legit to be used.The next step after the cointegration is confirmed is to run the DOLS Models.The outcomes of both DOLS models are shown in Table 2. Table 2 demonstrates that for the first model, the probability of the coefficient less from the alpha 10%, which means that the coefficient of Deforestation has positive and significantly impact on Carbon Dioxide and it significantly influence Carbon Dioxide Emission.On the other words, higher level of deforestation creates higher level of Carbon Dioxide Emission.The coefficient indicates that when the deforestation increases by 1 %, the Carbon Dioxide Emission increases by 0,14%.When deforestation occurs, much of the carbon cannot be stored by trees and stay in the atmosphere as carbon dioxide, which contributes to climate change.
The results of second model shows that the probability of the coefficient less from the alpha 5%, in which it suggests that Forest Area Coverage has negative and significantly impact on Carbon Dioxide.It means that higher level of Forest Area Coverage (reforestation) creates lower level of Carbon Dioxide.The coefficient indicates that when the Forest Area Coverage (reforestation) increases by 1 %, the Carbon Dioxide Emission decreases by 4%.More effort on reforestation means more woods in the forest.Then the forests will sequester (or absorb) and store carbon dioxide from the atmosphere, helping reduce emissions.The result of both models suggest that reforestation is important to reduce level of Carbon Dioxide In addition, there is no impact of income per capita on the level of Carbon Dioxide.It is suggested that the model should apply Environmental Kuznets Curve (EKC) to assess its impact on Carbon Dioxide (See, for example [8], [9], and [10]).The study indicates that Population seems to be a problem for the degradation of the environment.The coefficient indicates that when the deforestation increases by 1 %, the Carbon Dioxide Emission increases by 3.07% and 3.50% in model 1 and model 2 respectively.It means that more population creates more pollution air in Indonesia.The birth control without harm human right is the best solution for this problem.Lastly, there is no impact of inflation on the level of Carbon Dioxide.

Conclusion
The awareness of the significance of the reforestation to protect of the environment has become a main objective in this study.In conclusion, forest is undeniably vital keys to keep the future of the of Carbon Dioxide Emission in Indonesia in low level.Forests are crucial for the health of earth, as it is known as "lungs of the planet" because they generally absorb carbon dioxide and produce oxygen.It is also important to keep more forests in Indonesia because forests supply food and shelter for so much of life in Indonesia.Millions of people depend on forests as their main food sources and as their home.This study also shows that more population create more Carbon Dioxide Emission.In term of policy implications, some suggestions for the Indonesian government are (1) enforces reforestation as it is the best way to prevent deforestation, (2) applies more strict punishment for illegal logging, (3) promotes inclusive and sustainable forest management; and (4) apply the birth control without violate human right.

Table 1 .
The Results of Cointegration Test

Table 2 .
Outputs of Dynamic Ordinary Least Square