A Systems Engineering Approach to Decarbonizing Mining: Analyzing Electrification and CO2 Emission Reduction Scenarios for Copper Mining Haulage Systems

Abstract

Due to climate change risks, the public, regulators, and investors require solid actions to minimize the greenhouse gas (GHG) emissions of mineral extraction and metals production. The mining sector considers alternatives to reduce its carbon footprint by transforming the business and adopting new technologies into operations. Given the capital intensity, technical characteristics, and business structure involved, a shift in the mining industry necessitates impartial insights into the trade-offs and risks. Considering the low-carbon transition trade-offs and risks in mining, this study presents the application of system dynamics modeling (SDM) in mining projects to analyze the impact of decarbonization alternatives with respect to carbon footprint and costs. A system dynamics model of an open-pit copper mine is developed to quantify greenhouse gas (GHG) emissions, as well as capital and operational costs, during the project life cycle. The change in GHG emissions in the business-as-usual scenario with diesel equipment haulage versus the alternative scenario with electric overland conveyor haulage is compared concerning GHG emissions and associated costs. The results unequivocally demonstrated that electrifying material mobility offers significant decarbonization in open-pit mining if the on-site electricity has a low emission factor. The findings also indicate that the substantial cost difference between electrification and diesel alternatives is another major obstacle to implementing electrification in an open-pit copper mine. This research proves that implementing SDM in the mining industry can offer impartial insights into decision-making and enable a thorough evaluation of options using quantitative criteria. It effectively assesses and communicates the trade-offs and risks of transitioning to low-carbon alternatives because it analyzes project variables quantitatively and holistically and is easy to run.