Bamboo residues represent an abundant, renewable biomass feedstock that can be converted into hard carbon—an emerging anode material for sodium-ion batteries. This study presents a detailed techno-economic analysis of hard carbon production from bamboo residues across China’s ten most bamboo-rich provinces. Regional feedstock availability was estimated from provincial production statistics, while average transportation distances were derived using a square-root-area-based approximation method. The process includes hydrothermal pretreatment, acid washing, carbonization, graphitization, and ball milling. Material and energy inputs were estimated for each stage, and both capital and operating expenses were evaluated using a discounted cash flow model assuming a 15% internal rate of return. The resulting minimum selling price of bamboo-derived hard carbon ranges from 14.47 to 18.15 CNY/kg. Assuming 10% of bamboo residues can be feasibly collected and processed, these ten provinces could collectively support an annual hard carbon production capacity of approximately 1.04 million tons. The results demonstrate that bamboo residues are a strategically distributed and underutilized resource for producing cost-competitive hard carbon at scale, particularly in provinces with existing bamboo industries and supply chains.

Biomass-derived sulfur-containing hard carbons are promising anode candidates for sodium-ion batteries, but cross-study optimization remains difficult because reported electrochemical performance reflects both synthesis history and incomplete or non-uniform structural characterization. Here, we assembled a focused literature-derived dataset of 101 records from 16 journal articles and compared the predictive value of three information sources: precursor descriptors, process variables, and measured structural descriptors. We further introduced domain-knowledge-guided precursor descriptors to encode interpretable aspects of precursor chemistry and architecture, including lignin-related richness, polysaccharide contribution, volatile tendency, precursor-component coupling, and post-treatment category. In controlled feature-set comparisons, the model combining precursor and process descriptors achieved an R² of 0.59, outperforming the conventional combination of process and structural descriptors (R² = 0.57) and remaining close to the full-information setting (R² ≈ 0.61). Model interpretation further showed that, when structural descriptors were removed, predictive reliance shifted toward precursor and process variables, indicating that accessible upstream descriptors retain a meaningful fraction of the formation-pathway information relevant to sodium storage. These results should be interpreted within this curated sulfur-containing literature space rather than as a universal predictor, but they demonstrate that domain-knowledge-guided precursor encoding can support low-characterization, screening-oriented prediction and experimental prioritization.