Biochar, a carbon-enriched material derived from pyrolyzed biomass, has evolved from an ancient farming practice into a mature carbon sequestration technology, emerging as a pivotal strategy for achieving carbon neutrality. Nevertheless, heterogeneous production methodologies, multifactorial interactions, and diverse application scenarios pose significant challenges in establishing reliable assessment frameworks for practical implementation. From the perspective of biochar lifecycle and supplemented by data analysis, this critical review focuses on the key influencing factors of biochar applied in three main carbon-negative areas (including soil, construction, and carbon absorption), along with their influence mechanisms, challenges, and further prospects. In addition, the study evaluates machine learning applications in biochar research, highlighting their predictive optimization capabilities for tuning operational parameters and guiding precise application. These computational advances establish a paradigm shift toward precision-controlled biochar deployment in heterogeneous environmental systems.
Yu et al. (2025) studied this question.