AI-enabled Decarbonization Framework

Abstract

Eight major supply chains contribute to more than 50% of the global greenhouse gas emissions (GHG). These supply chains range from raw material to end-product manufacturing. Hence, it is critical to accurately estimate the carbon footprint of these supply chains, identify GHG hotspots, explain the factors that create the hotspots and carry out what-if analysis to reduce carbon footprint of supply chains. Towards this, we propose an enterprise decarbonization accelerator framework with a modular structure that automates carbon footprint estimation, identification of hot spots, explainability and what-if analysis to recommend measures to reduce carbon footprint of supply chains. To illustrate the working of the framework, we apply it to the cradle to gate extent of palm oil supply chain of a leading palm oil producer. The framework identified that the farming stage is the hot spot in the considered supply chain. As the next level of analysis, the framework identified the hotspots in the farming stage and provided explainability on factors that created hotspots. We discuss the what-if scenarios and the recommendations generated by the framework to reduce the carbon footprint of the hotspots and the resulting impact on palm oil tree yield.