Time-Aware Explainable Machine Learning for CO2 Emission Analysis in Bangladesh: Decoupling, Decomposition, and 2030 Scenarios

In Bangladesh, the continuing economic and energy-demand growth has been paralleled by the continuing increase in emission intensity across the country’s territory, which raises the question of what territorial CO2 emissions and economic growth mean together, and how these can be understood and anticipated. This study proposes an analytical framework to explain and understand the CO2 and Greenhouse Gas indicators from the Our World in Data database in a timeaware way, covering the description of 1990-2024and completecase modelling over 1990–2022 (n = 33). Gross domestic product, population, and primary energy consumption are used to predict territorial CO2 emissions with Linear Regression, Ridge Regression, Random Forest, and XGBoost, evaluated under both repeated random cross-validation and an expandingwindow temporal-validation protocol that mimics future-year forecasting. Model behavior is interpreted with SHAP and permutation importance and ablation (leave-one-feature-out) methods; historical structural dynamics are explored with Tapio decoupling and Kaya–LMDI decomposition; and 2030 scenarios are projected with Monte-Carlo. The best model is validated by random cross-validation with R2 ≈ 0.996, and by expanding window temporal validation with R2 ≈ 0.974, suggesting that indistribution fit overestimates the out-of-distribution fit for this short annual series. Primary energy consumption turns out to be the most explanatory driver in the SHAP analysis, while the decomposition of the emissions increase by Kaya–LMDI reveals an affluence effect of +71.93 MtCO2. With various growth roadmaps, emissions are estimated to be around 158.8–187.1 MtCO2 in the illustrative 2030 scenarios. The finding suggests that temporal (not only random) validation of the credibility of forecasting is essential, and that the emissions pathway in Bangladesh is still on a trajectory more about economic scale and less about structural decoupling.