Optimizing Sustainability: A Deep Learning Approach on Data Augmentation of Indonesia Palm Oil Products Emission

Abstract

Abstract Life Cycle Assessment (LCA) is a widely used methodology for quantifying the environmental impacts of products, including the carbon footprint. However, conducting LCA studies for complex systems, such as the palm oil industry in Indonesia, can be challenging due to limited data availability. This study proposes a novel approach called the Anonymization Through Data Synthesis (ADS-GAN) based on a deep learning approach to augment carbon footprint data for LCA assessments of palm oil products in Indonesia. This approach addresses the data size limitation and enhances the comprehensiveness of carbon footprint assessments. An original dataset comprising information on various palm oil life cycle stages, including plantation operations, milling, refining, transportation, and waste management. The number of original data is 195 obtained from the Sustainable Production Systems and Life Assessment Research Centre of Indonesia's National Innovation Research Agency (BRIN). To measure the performance of prediction accuracy, this study used regression models: Random Forest Regressor (RFR), Gradient Boosting Regressor (GBR), and Adaptive Boosting Regressor (ABR). The best-augmented data size is 1000 data. In addition, the best algorithm is the Random Forest Regressor, resulting in the MAE, MSE, and MSLE values are 0.0031, 6.127072889081567e-05, and 5.838479552074619e-05 respectively. The proposed ADS-GAN offers a valuable tool for LCA practitioners and decision-makers in the palm oil industry to conduct more accurate and comprehensive carbon footprint assessments. By augmenting the dataset, this technique enables a better understanding of the environmental impacts of palm oil products, facilitating informed decision-making and the development of sustainable practices.