Quantifying Drivers of GHG Emissions in ASEAN: Modeling CO2 Emissions Using LMDI and ARIMAX Approaches

Abstract

This work aims to create a robust causal regression model that can accurately measure the influence of many variables on greenhouse gas (GHG) emissions in ASEAN nations from 1971 to 2017. The study aims to identify the main factors contributing to emissions and provide valuable information for implementing effective reduction methods. This is important because balancing economic growth and environmental sustainability in the fast- changing ASEAN area is crucial. The research used the Logarithmic Mean Divisia Index (LMDI) decomposition method to examine the individual contributions of various causes to changes in CO2 emissions over time. In addition, a model called ARIMAX (Autoregressive Integrated Moving Average with Exogenous Variables) is created to anticipate CO2 emissions and determine essential factors that influence them. The dependent variable is total GHG emissions measured in metric tons of CO2 equivalent, while independent variables include GDP, energy intensity, carbon intensity, and population growth. The study discovered that GDP, with varied degrees of impact, is the primary catalyst for CO2 emissions in ASEAN nations. The energy intensity is projected to decline, indicating increases in efficiency, while the influence of population growth is forecast to be positive but less substantial compared to economic reasons. The research is anticipated to uncover disparities in the decrease of carbon intensity and the efficacy of policies across ASEAN nations, with more advanced economies demonstrating more resilient systems. These results provide significant knowledge for governments and companies, aiding in creating focused efforts to reduce emissions and improve carbon accounting standards across the ASEAN region.

Keywords

• Autoregressive Integrated Moving Average with Exogenous Variables, Carbon Accounting, Greenhouse Gas Emissions, Logarithmic Mean Divisia Index, Emission Drivers