Distributed biochar and bioenergy coproduction: a regionally specific case study of environmental benefits and economic impacts

Abstract

Biochar has been advocated as a method of sequestering carbon while simultaneously improving crop yields and agro-ecosystem sustainability. It can be produced from a wide variety of biomass feedstocks using different thermochemical conversion technologies with or without the recovery of energy coproducts, resulting in chars of differing quality and a range of overall system greenhouse gas (GHG) mitigation outcomes. This analysis expands on previous sustainability studies by proposing a mechanistic life cycle GHG and economic operating cost assessment model for the coproduction of biochar and bioenergy from biomass residue feedstocks, with a case study for north-central Colorado presented. Production is modeled as a continuous function of temperature for slow pyrolysis, fast pyrolysis, and gasification systems. Biochar environmental benefits (C sequestration, N₂O suppression, crop yield improvements) are predicted in terms of expected liming value and recalcitrance. System-level net GHG mitigation is computed, and net returns are estimated that reflect the variable economic costs of production, the agronomic value of biochar based on agricultural limestone or fertilizer displacement, and the value of GHG mitigation, with results compared to the alternate use of char for energy production. Case study results indicate that slow pyrolysis systems can mitigate up to 1.4 Mg CO₂eq/Mg feedstock consumed, provided a favorable feedstock is utilized, production air pollutant emissions are mitigated, and energy coproducts are recovered. The model suggests that while financial returns are generally greater when char is consumed for energy (biocoal) than when used as a soil amendment (biochar), chars produced through high-temperature conversion processes will have greater GHG-mitigation value as biochar. The biochar scenario reaches economic parity at carbon prices as low as $50/Mg CO₂eq for optimal scenarios, despite conservative modeling assumptions. This model is a step toward spatially explicit assessment and optimization of biochar system design across different feedstocks, conversion technologies, and agricultural soils.

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