Descriptive and predictive analyses of carbon emissions in Indonesia: a multifaceted approach incorporating stock market and commodity prices - Alvin Muhammad 'Ainul Yaqin; Auliya Aisyah Putri; Vridayani Anggi Leksono; Noni Oktiana Setiowati; Remba Yanuar Efranto; Hilwa Dwi Putri Nabiha (Carbon Management)
This study examined the relationship between carbon emissions, stock market fluctuations, and key sector commodity prices in Indonesia. The nation’s carbon emissions have surged due to economic growth, energy dynamics, transportation advancements, and infrastructure expansion, primarily reliant on carbon-intensive fuels. To support mitigation strategies, we explored the influence of economic growth, energy consumption, transportation and logistics, and infrastructure through structural equation modeling (SEM). The SEM model fitted well, demonstrating the significant impact of the transportation and logistics (p-value of .016) and infrastructure investments (p-value of .002) on carbon emissions. Predictive analysis using long short-term memory (LSTM) and multiple linear regression (MLR) revealed LSTM’s superior performance in forecasting carbon emission levels, with a root mean square error (RMSE), mean absolute percentage error (MAPE), and coefficient of determination (R2) of 7.07E-07, 14.80%, and 0.8550, respectively. In contrast, MLR exhibited an RMSE, MAPE, and R2 of 0.8138, 99.95%, and 0.6897. This research underscores the critical roles of the transportation and logistics sectors strategic infrastructure investments in shaping carbon emissions while highlighting LSTM’s effectiveness in predictive analysis. These insights are crucial for policymakers and stakeholders to formulate effective strategies for mitigating carbon emissions and promoting sustainable development in Indonesia.