## 5 Discussion  
AHP is an analysis method that calculates relative importance by categorizing multiple evaluation criteria into a hierarchy when they are complex. In the case of complex evaluation criteria like EIA, the use of AHP is appropriate and has been effectively used in many studies [9; 40; 41; 42; 43]. The KEIDP is also produced using the AHP approach, and it was the result of reflecting the various opinions of many experts (Table 1). However, as input data for KEIDP calculation were no longer produced, there was a limit to the EIA for the development projects using KEIDP. Although it is possible to indicate the impact of a development project on the environment from the conditions before and after the project using EIA Big Data, there is a limit to the scope of the analysis due to the characteristics of the independent EIA Big Data. Following the proposed normalization method, it was possible to analyze the proportion of environmentally friendly projects, but it was also difficult to show the overall trend. Therefore, to comprehensively analyze the impact of development projects on the environment, it was necessary to utilize the existing KEIDP.  
The MLP-ANN approach that learns the relationship between input data and reference data with multiple nodes and layers can be used to learn or predict AHP results [44; 45]. In this study, the KEIDP calculated from the AHP method was learned by producing the MLP-ANN model using EIA Big Data as input data (Figure 2 and Table 2). There were few development projects with KEIDP and EIA Big Data, but the trained data can explain the whole development project with a margin of error of about 7% at a 95% confidence level [46]. After completing the learning, it was confirmed that the MLP-ANN model to calculate the KEIDP using EIA Big Data was effective. Comparing the estimated KEIDP and the true KEIDP, the correlation coefficient was 0.957 and the RMSE was 0.024 (Figure 3b).  
The application of KEIDP is steadily increasing, indicating that development projects are considering their environmental impacts by taking their EIA results seriously (Figure 5). However, despite the upward trend in KEIDP values for each year, the KEIDP deviations for each development project and management agency were high (Figure 6). It was found that about 68% of the projects (the standard deviation of the KEIDP distribution was within 1) each year had KEIDP values that changed over a range of about 0.2. The reason for this is likely to be because the items and criteria used by each management agency for evaluating the environmental impact of development projects are different when performing an EIA. In the future, it will be necessary for all management agencies to apply the KEIDP calculation model to minimize the impact of development projects on the environment and reduce the deviations between development projects through continuous monitoring. In addition, it is necessary to analyze the cause by evaluating the EIA performed for the projects with low KEIDP, and further research is needed to improve or effectively apply the EIA according to the characteristics of the development projects.  
Although the scope of the study was limited to Korea, the methodology can be applied to development projects in other countries as well [47; 48]. If data such as EIA Big Data can be obtained in other countries (even if they do not perfectly match all the data types used in this study), it is expected that EIA can be performed through this methodology.