# Environmental Impacts of Cement Production: A Statistical Analysis
## 4 Discussion  
Due to a dependence on fossil fuels and the calcination of raw materials, the cement industry generates about 5% of global greenhouse gas emissions. Within this framework, several efforts are on-going to protect the environment and increase energy efficiency using renewable resources or alternative fuels. In order to analyze comparable environmental performances, cement companies are conducting life cycle assessment of their \"from cradle to gate\" processes in order to identify the best strategies to meet the need for sustainable development.  
In this study, having regard to the European approach compliant with the standard EN 15804, the environmental impacts of 193 different recipes of gray cement produced in Italy from 2014 to 2019 have been assessed. Fifteen different impact categories have been considered and split into two classes, \"Emissions\" and \"Consumption\". One of the main results of this work concerns the identification of the significant Consumption ICs to predict the behavior of Emission ICs, In particular, the target of this paper consists in answering to the following questions:  
1. Which ones are the most relevant variables among the Consumption ICs in order to explain the behavior of GWP?
2. Are different variables important to predict GWP for the four types of cement?
3. Are the relevant variables to predict GWP also useful to predict other Emission ICs?  
As far as Question 1 is concerned, it is shown that the most important variable to predict the behavior of GWP is ADPf (Figure 8), while NRSF and RSF are the two other most relevant consumption variables. To answer Question 2, a more accurate model is produced by fitting a linear regression and applying the AIC criterion for different types of cement (i.e., CEM I, CEM II, CEM IV) separately. Also in this case, ADPf is proved to be the most important Consumption IC. However, scatterplots related to GWP and the Consumption ICs show that the environmental performances of CEM I differ from those of the other types, and their values are higher. Predictive models built separately for each type of cement revealed more accurate predictions for GWP. Finally, concerning Question 3, the authors investigated if the relevant variables to predict GWP could predict other Emission ICs. In this case, it is shown that fitting separately regression models and selecting the most important variables leads to more accurate predictions for all the other Emissions ICs (Table 25) in comparison to the standard linear model or the one which uses the same Consumption ICs for GWP. Also in this case, ADPf is confirmed to be a strong predictor in the models related to the emission variables AP, EP, POCP (for CEM I and CEM II), and P (for CEM I and CEM IV). Therefore, the obtained results underline the need for policies and strategies that could reduce consumption of energy, both fossil and secondary fuels, and justify the European policies about Emission trading and Best Available Techniques to be implemented in the cement industry.  
Conceptualization, C.D. and L.M.; methodology, C.D.; software, C.D. and L.M.; validation, L.M. and C.D.; formal analysis, C.D.; data curation, L.M.; writing--original draft preparation, C.D. and L.M.; review and editing, C.D. and L.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.