The worldwide cement industry plays an important role in addressing the climate change challenge. Brazil’s cement industry currently has 91 cement plants with an installed production capacity of 94 million tons per year and has started to calculate the net CO₂ emissions to achieve a carbon-neutral cement sector by 2050. Accordingly, the carbon dioxide uptake due to mortar and concrete carbonation is subtracted from the carbon dioxide emitted by the chemical reaction for the calcination of lime, i.e., the calcination process performed during clinker production. Now-adays, the Intergovernmental Panel on Climate Change (IPCC) Guidelines for National Greenhouse Gas (GHG) Inventories to report the GHG emissions do not include any calculation procedure to consider the mortar and concrete carbonation. However, the Intergovernmental Panel on Climate Change (IPCC)’s Sixth Assessment Report (AR6) recognizes the physico-chemical process known as carbonation. Brazilian net carbon dioxide emissions of cements produced from 1990 to 2019 are estimated considering the carbon dioxide uptake during the service-life and end-of-life and secondary usage stages (Tier 1). This is a fundamental scientific and technological novelty that changes the current approach to estimate the carbon dioxide emissions due to the Portland cement clinker production. Even considering the relative novelty of this approach, it should be promoted in the future and included in the national inventory report (NIR). The carbon dioxide uptake by mortar and concrete carbonation for 30 years is about 140 million tons. Within this thirty-year period about 483 million tons have been released due to the calcination process.

Lately, several machine learning (ML) techniques are emerging as alternative and efficient ways to predict how component properties influence the properties of the final mixture. In the area of civil engineering, recent research already uses ML techniques with conventional concrete dosages. The importance of discussing its use in the Brazilian context is inserted in an international context in which this methodology is already being applied, and it is necessary to verify the applicability of these techniques with national databases or what is created from national input data. In this research, one of these techniques, an artificial neural network (ANN), is used to determine the compressive strength of conventional Brazilian concrete at 7 and 28 days by using a database built through publications in congresses and academic works and comparing it with the reference database of Yeh. The data were organized into nine variables in which the data samples for training and test sets vary in five different cases. The eight possible input variables were: consumption of cement, blast furnace slag, pozzolana, water, additive, fine aggregate, coarse aggregate, and age. The response variable was the compressive strength of the concrete. Using international data as a training set and Brazilian data as a test set, or vice versa, did not show satisfactory results in isolation. The results showed a variation in the five scenarios; however, when using the Brazilian and the reference data sets together as test and training sets, higher R² values were obtained, showing that in the union of the two databases, a good predictive model is obtained.