A comparative study of time series and machine learning methods in predicting Indonesia’s greenhouse gas emissions

Rizki Kusumawardani, Melani Yusi Aryanda, Annisa Zahrotu Firda Asfari, Luthfiya Zuhura Syifa Fuadah

Abstract

Greenhouse gas emissions are a contributing factor to global warming and climate change, which have widespread environmental, social, and economic impacts. As a developing country, Indonesia contributes significantly to greenhouse gas emissions, particularly from the energy, transportation, and land use sectors, while also being vulnerable to their impacts. In line with global commitments, Indonesia has set a target of reducing emissions by 31.89% by 2030 within its Nationally Determined Contribution (NDC) framework. However, the facts show that Indonesia's greenhouse gas emissions trend remained volatile between 2000 and 2019, with a tendency to increase more than decrease. This indicates that without strong and consistent policy intervention, the 31.89% emission reduction target by 2030 will likely be difficult to achieve. Therefore, predictive modeling of residential emissions is necessary to produce the most robust and accurate model. These predictions are expected to serve as a reference for the government in setting policy priorities and formulating strategic steps to address greenhouse gas emissions in a more targeted, measurable, and effective manner. Predictive modeling of greenhouse gas emissions was conducted using a quantitative research approach, with greenhouse gas emissions as the primary variable. Data used were from the Badan Pusat Statistik (BPS) covering the period 2000 to 2019. The modeling methods employed were time series modeling and machine learning. The two models were then compared to determine the best model to use as a basis for greenhouse gas emission control efforts.

Keywords

greenhouse gas emission; machine learning; prediction; time series

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References

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DOI: https://doi.org/10.20961/ijsascs.v9i1.126606

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