Modeling and Forecasting of Sulfur Dioxide (SO₂) Emissions in Several ASEAN Countries (Using State Space Multivariate Time Series Analysis)

Mustofa Usman (Department of Mathematics, Universitas Lampung, Bandar Lampung, Lampung, 35141, Indonesia)
Edwin Russel (Department of Mathematics, Universitas Lampung, Bandar Lampung, Lampung, 35141, Indonesia)
Nurhanurawati (Department of Mathematics Education, Universitas Lampung, Bandar Lampung, Lampung, 35141, Indonesia)
Faiz AM Elfaki (Department of Mathematics, Statistics and Physics, Qatar University, Doha, 2713, Qatar)
Nadya R. Ikhsana (Departement of Economic Development, Universitas Lampung, Bandar Lampung, Lampung, 35141, Indonesia)
Deta Erviana (Graduate Students, Department of Mathematics, Universitas Lampung, Bandar Lampung, Lampung, 35141, Indonesia)
Moni Dwi Fenski (Graduate Students, Department of Mathematics, Universitas Lampung, Bandar Lampung, Lampung, 35141, Indonesia)

Science and Technology Indonesia
Vol. 11 No. 3 (2026): July

Sulfur dioxide (SO2) emissions (ton/year) produced from coal combustion, household heating, motor vehicles, and volcanic eruptions, has been considered as a dangerous air pollutant that causes many respiratory diseases and increases the mortality rate. Many studies have been conducted generally in developed countries and industrialized countries that are aware of the adverse effects of increasing SO2 emissions in the air. Many studies have been conducted to measure the level of air pollution caused by SO2 emissions and research on the relationship of SO2 emissions with public health and mortality rates. The problem in this study is how to build the best State Space Multivariate Time Series model for SO2 emissions data in several ASEAN countries, Indonesia, Thailand, Philippines and Malaysia. This study aims to build the best State Space Multivariate Time Series model that fits the data and uses the best state space model for forecasting SO2 emissions for the next few years. The analysis method that will be used is State Space Multivariate Time Series Analysis (Autoregressive Vector modeling, and State Space Model). The results show that SO2 emissions in Indonesia are significantly influenced by emission conditions in Indonesia four years earlier and SO2 emissions in Malaysia two and four years earlier; SO2 emissions in Thailand are significantly influenced by SO2 emission conditions in Thailand and the Philippines one year prior; SO2 emissions in the Philippines are significantly influenced by SO2 emission conditions in Thailand one years prior and SO2 emission conditions in the Philippines four years prior; SO2 emissions in Malaysia are significantly influenced by SO2 emissions conditions in Indonesia two, three, and five years prior, SO2 emissions conditions in Malaysia four years prior, SO2 emissions conditions in Thailand and Philippines five years prior. Forecasting results using the state space model indicate a downward trend in SO2 emissions in Indonesia, Thailand, the Philippines, and Malaysia over the next ten years.