关键词: 生物固碳效益评价, 基因组规模代谢网络, 生命周期评价, 机器学习, 原子经济性, 多层次评价框架

Abstract: With the concentration of carbon dioxide (CO2) in the air rising, the threat of global warming is getting worse. Due to its mild conditions and reaction specificity, the biological carbon sequestration technology shows excellent potential for industrial applications under carbon neutral constraints. However, to be genuinely sustainable, industrial applications must implement low-carbon-footprint technology. Current methods for biological carbon sequestration assessment are few and heterogeneous, with the lack of a harmonized scientific assessment framework. In order to promote the development on the biological carbon sequestration assessment, this article reviews the application of genome-scale metabolic network model (GSM) and life cycle assessment (LCA) in biological carbon sequestration assessment, with a focus on analyzing the bottleneck issues of these two methods in biological carbon sequestration assessment. In addition, this review also provides a systematic outlook on the future development direction of the biological carbon sequestration assessment, elaborates on the significant advantages of machine learning applications, points out the scientificity of evaluation indicators based on atomic economy (AE), standardizes the basic framework for data acquisition, and analyzes important breakthroughs in constructing a multi-level evaluation framework.

Key words: evaluation of biological carbon sequestration benefits, genome-scale metabolic network models, life cycle assessment, machine learning, atomic economy, multi-level evaluation framework