Developing a framework towards global biochar supply chains to optimize regional production cost

Abstract

The expansion of carbon dioxide removal (CDR) strategies is essential to achieve the climate targets. Among emerging CDR technologies, biochar holds particular promise due to its stable carbon storage and multiple co-benefits. While previous studies have examined the global potentials of biochar production, comprehensive assessments that include cost structures and spatial variability remain limited. This study addresses this gap and presents a comprehensive that integrates geospatial machine learning with techno-economic analysis to estimate region-specific biochar production costs at a global level. The derived approach estimates the biomass yields of various lignocellulosic biomass sources using an XGBoost machine learning model trained on climate and soil data. Roadside production costs are then calculated based on resource input parameters, followed by transport cost estimations using spatial distance metrics. Finally, pyrolysis costs are included to derive the total production cost of biochar per ton across regions globally. The results show substantial regional variation, with total production costs ranging from 113 to over 1500 €/ton. Sub-Saharan Africa, Latin America, and South Asia demonstrate the lowest median costs, below 300 €/ton, primarily due to low labor and biomass costs. Eucalyptus emerges as the most cost-efficient biomass provided it is cultivated. While the Kontiki flame curtain kilns are more cost efficient in low-income regions, advanced-technology plants become competitive in industrialized areas, especially when district heat is considered. These insights are crucial for guiding investments and policies that aim to expand biochar use as a viable and cost-effective CDR pathway.