## 5 Discussion  
In order to calculate the new intensity prediction equation (IPE), it is crucial to analyze the role of input data. The data quality and quantity significantly influence the final result, emphasizing the relevance of the data collection phase. For instance, the new database on the environmental effects of the 1980 Iripinia-Basilicata earthquake [38] exemplifies the importance of enhancing the quantity and quality of available data. This database allows for a detailed examination of the spatial distribution of intensity levels across the affected region, providing valuable, spatially continuous, and homogeneous insights into the impact of the earthquake. As illustrated in Figure 4b, the final output, in terms of attenuation, based on this new database, markedly differs from previous data inputs [37]. Several factors contribute to this difference. Notably, datasets with fewer points tend to highlight \"maximum\" effects over \"typical\" effects. While this approach is beneficial for a conservative analysis, it obscures details on \"average\" versus \"giant\" values [60]. Given that IPEs and GMPEs (Ground Motion Prediction Equations) are foundational for hazard maps and scenario definitions, it is essential to determine whether input parameters are  
Figure 8: (**a**) Representation of IDP median values obtained from intensity binning, and the proposed IPE based on the horizontal distance from the rupture, D\({}_{\text{RupX}}\) (with confidence bounds of 95%); (**b**) residual plot of the data (IDPs) analyzed in this work. based on average or maximum values. For lower degrees of intensity, such as I = V or VI ESI-07, data incompleteness bias at large distances is often cited. However, this bias can also occur at short distances; for instance, focusing solely on the strongest effects in the epicenter area neglects lower intensity effects at short distances. Consequently, if data quality or quantity are compromised in the far field, the median distance decreases, whereas if this occurs in the near field, the median distance increases. This observation explains the findings related to \"Aim 1\" of this work (Figure 4b). Observing the blue points that represent the median intensity values, the new 1980 Irpinia-Basilicata dataset exhibits steeper attenuation due to the abundance of ESI-07 V-VI points at short distances. This scenario underscores the importance of evaluating the minimum intensity threshold to consider the data as reliable. The choice on the minimum ESI-07 threshold impacts the number of \(\Delta\)I classes available for regression purposes; in this study, we set this threshold to ESI-07 \(\geq\) V, which we consider as the best compromise between data quality/availability and statistical rigor. In order to obtain large datasets and compare events in various seismotectonic and climatic settings, it is important to build standardized datasets. Several efforts have been recently realized for surface ruptures and earthquake-induced landslides [20,61]. However, currently, these data (i) refer to a single environmental effect at a time and (ii) have no ESI-07 information. A future prospect could be to create an integrated dataset on all the environmental effects induced by an earthquake, similar to what is implemented in the Earthquake Environmental Effects Catalogue [39]. Moreover, Figure 4b shows that methodological choices can also influence the final result. The data obtained from the new 1980 Irpinia-Basilicata database revealed a marked difference between the values derived from the IDP-based method and those calculated using the grid-based method. This discrepancy can be attributed to the fact that the IDP-based method has fewer values at large distances and that delineating isoseismals using the grid-based method may involve a more subjective component compared to evaluating the intensity of individual IDPs.