As the second largest global carbon dioxide emission source, the cement industry contributes approximately 8% of total anthropogenic carbon emissions. Under the "dual-carbon" strategic framework, developing novel low-carbon cementitious materials emerges as a research priority in civil engineering. Alkali-activated cementitious materials (AACM) are regarded as a promising alternative to conventional Portland cement due to their advantages of eliminating high-temperature calcination and utilizing industrial solid wastes. However, the existing research predominantly focuses on material performance optimization, while the definition of their "low-carbon" attributes remains limited to raw material substitution, lacking a systematic life cycle carbon emission accounting framework. No scientific evaluation methods or quantitative models are established to assess carbon footprint disparities among different activator systems (i.e., carbonate, hydroxide, and silicate). Furthermore, red mud-a massive solid waste generated by the alumina industry with global annual emissions exceeding 150 million tons exhibits natural compatibility with alkali-activated systems due to its high alkali metal content. Nevertheless, the existing studies predominantly emphasize its reactivity activation, while neglecting the development of integrated low-carbon processing technologies. Establishing systematic carbon accounting methodologies and elucidating the coupling mechanisms among activators, carbon emissions, and mechanical properties become critical scientific challenges for advancing the engineering applications of alkali-activated materials.

In the AACM production framework, the study defined a "cradle-to-gate" system boundary spanning from raw material acquisition to final product formation. This boundary was divided into three distinct phases, i.e., raw material acquisition stage (Am), encompasses processes such as raw material production, mining, and extraction, and raw material transportation stage (At). Accounts for carbon emissions generate during the transportation of all required raw materials. Production stage (Ap) includes operational steps such as drying, homogenization, grinding, storage, and packaging. The investigation specifically employed red mud as an intrinsic alkali source, focusing on elucidating the influence patterns of both red mud and external alkalis (i.e., Na₂CO₃, NaOH, and Na₂O·2SiO₂) on the carbon emissions of AACM. Response surface methodology (RSM) with the Box-Behnken design was employed to optimize mix parameters (i.e., Na₂CO₃ dosage, red mud content, and cement content), conducting multi-factor interaction analysis with dual objectives, i.e., 28-d compressive strength and carbon emissions.

To compare the life cycle carbon emissions of AACM production in the same production line with industrial solid waste (i.e., red mud), granulated blast furnace slag, and ordinary Portland cement clinker as precursors, as well as Na₂CO₃, NaOH, and Na₂O·2SiO₂ as activators and to design low-carbon AACM, the following parameters were established. For the raw material transportation phase, heavy-duty diesel trucks (46-ton payload) with a carbon emission factor of 0.057 kg CO₂/(t·km) were selected, assuming a default transportation distance of 1000 km. For the production phase, the carbon emissions from drying red mud and granulated blast furnace slag were estimated based on electricity consumption of drying equipment and exhaust fans. The field data indicated that drying granulated blast furnace slag with 10%–13% moisture content required approximately 11.5 kW·h per ton of wet material. For high-moisture red mud drying (using a Φ3 m×28 m rotary dryer with 2.45–4.20 r/min rotation speed and 3.5% slope), the energy consumption reached 35.0 kW·h per ton of wet material (data from Shanghai Bai Ao Heng New Materials Co., Ltd., China). Carbon emissions from material batching, grinding, and homogenization processes were estimated using reference power consumption data (i.e., ≈33 kW·h per ton of cement) from a plant of Southwest Cement Co., Ltd., China. The average carbon emission factor of China's regional power grids was adopted as 0.5366 kg CO₂/(kW·h) (2022 national average electricity CO₂ emission factor).

This study proposes a life cycle carbon footprint accounting model for AACM production, reveals the synergistic regulation mechanism of activator types and mix proportions on the material low-carbon characteristics and mechanical performance through response surface methodology-based optimization design and establishes a cradle-to-gate carbon footprint accounting system covering raw material acquisition, transportation, and production stages. The contribution weights of red mud-based AACM precursors and activator types to carbon emissions are quantified, providing a quantitative basis for evaluating low-carbon cementitious materials. As an endogenous alkali source, red mud can effectively reduce the dosage of external activators. Coupled with the low-carbon characteristics of Na₂CO₃ (carbon emission factor: 0.79 t CO₂/t), this significantly reduces the system's carbon emissions. At a red mud content of ≤50% and a cement clinker dosage of ≤10%, the material's carbon emissions can reduce to below 280 kg CO₂ per ton of AACM, validating a feasibility of the synergistic pathway of solid waste recycling–low-carbon process. The low-carbon design based on the RSM demonstrates that in the optimal mix proportion of the red mud-slag system (with 5.54% Na₂CO₃ dosage, 35.56% red mud, 57.22% slag, and 7.22% cement), the cementitious material achieves a 28-d compressive strength of 58.5 MPa and a carbon emission of 228 kg CO₂ per ton of AACM. This system exhibits both high strength and low-carbon characteristics, with performance comparable to Portland cement P·O 42.5R. In this mix proportion, red mud with a high alkali content provides abundant endogenous alkali to the system, reducing reliance on exogenous alkali. The amorphous phase structure of blast furnace slag endows it with a high alkali reactivity, enabling a rapid release of Ca²⁺ and SiO₄^4– under Na₂CO₃ activation to promote gel formation and enhance strength. The hydration of minerals such as C₃S and C₂S in cement generates additional Ca(OH)₂, further elevating the pH value of the system. This accelerates the depolymerization of slag vitreous phases and enhances the compactness of the matrix interfacial transition zone, thereby ensuring the mechanical performance of the AACM.

This study could pioneer the integration of red mud's intrinsic alkali properties with low-carbon activators, proposing a holistic "low-carbon design–carbon accounting–performance optimization" methodology. The framework provided a theoretical support for industrial applications of alkali-activated materials, offering dual benefits, i.e., large-scale red mud consumption in the alumina industry and a technical pathway for achieving the "dual carbon" goals in construction materials.

In light of the policy of ‘carbon peaking and carbon neutrality' and the advent of artificial intelligence, there is an urgent need for research and development in the field of cementitious materials to advance low-carbon alternatives. Given the lengthy research periods associated with traditional experimental methods, digital research and development has recently emerged as a dominant trend in the field. Currently, digital models are being developed for purposes such as performance prediction, composition optimization, and low-carbon innovation, with a primary focus on algorithm optimization. However, the success of these models relies heavily on the availability of accurate, high-quality databases, which serve as the foundation for model implementation. Utilizing such databases can significantly simplify model construction and reduce the need for extensive optimization procedures. Consequently, building comprehensive databases for cementitious materials research and development has become a key objective in this field.

This paper reviews the current status of databases for cementitious materials, including those related to clinker, cement, concrete, mineral admixtures, and construction mortar. The types of data, application methods, and scope of application encompassed by these databases are summarized. Databases for clinker were among the earliest to be developed, primarily cataloging diverse mineral properties, including hydration products. They include crystallographic data, thermodynamic data, and force field data, with some databases integrating models for analyzing cement hydration. Cement databases, while recording thermodynamic properties, cement types, and characteristics, have largely been focused on production control in cement plants. Moreover, the construction of databases for concrete and related materials has often been driven by specific research objectives, such as studying chemical composition, strength, and durability. Some researchers have also conducted property predictions based on their collected datasets.

Despite the existence of numerous databases on cementitious materials, a significant volume of relevant data within scientific literature remains underutilized. The advancement of artificial intelligence in natural language processing has enabled the adaptation of data extraction algorithms across various domains, including metals, medicine, and biology. Named entity recognition and textual relationship extraction—two critical components of literature data mining—can be implemented through AI algorithms such as ChemDataExtractor and BERT. ChemDataExtractor and related algorithms have demonstrated accurate chemical data extraction from compounds and semi-supervised relationship extraction. Similarly, BERT, like ChatGPT, is a state-of-the-art language model developed using the Transformer architecture and has been successfully applied in automated text data extraction. However, the complex mineralogical and chemical composition, multi-scale particle characteristics, and hydration processes of cementitious materials pose challenges to the direct application of these algorithms.

The construction of comprehensive databases represents the cornerstone of the digital transformation of low-carbon cementitious material research and development. Although existing databases contain extensive datasets on cementitious materials, several challenges persist, such as non-standardized database structures, insufficiently considered data categories, and incomplete material coverage. To address these issues, future database development should prioritize unifying data formats and linking upstream and downstream processes to create a cohesive and interconnected database. Such a unified database would enable the establishment of a fully connected data chain for cementitious materials, enhancing the accuracy of predictions, supporting reverse design processes, and saving time on data cleaning.

Particular emphasis should also be placed on refining databases for related materials, such as mineral admixtures. These refined databases would provide critical data for improving the durability of cementitious materials and reducing their carbon emissions. Furthermore, combining artificial intelligence algorithms, such as ChemDataExtractor and BERT, with domain expert knowledge holds significant potential for advancing literature mining techniques tailored to cementitious materials. This process could begin by focusing on individual performance attributes and gradually expand to encompass the comprehensive extraction of data for all cementitious materials.

The future of cementitious materials database development is promising, with the potential to drive innovations in low-carbon materials research and development, ultimately contributing to achieving global carbon neutrality goals.