# The Use of Artificial Intelligence for Assessing the Pro-Environmental Practices of Companies
## 3 Results
### Computational Analysis  
ANNs directly convert survey data linked to company size into a set of percentages for four categories of company descriptions: (1) product design, (2) product manufacturing, (3) product use, and (4) product recycling. It is therefore used not only to simply extract rules linking output to input data and rapidly estimate the survey results, but to also auto  
Figure 2: Implementation of environmental solutions in enterprises.  
matically or semi-automatically predict the state of the enterprise in the above-mentioned four categories with deliberate or independent modifications of one or more input parameters. This lays the foundation for a digital twin of the environmental friendliness of the enterprise, fed automatically with data within the Industrial Internet of Things (IIoT), on which future modifications can be tested without the need to switch, for example, the entire production line.  
In the model, each layer of the network contained neurons with the same activation function (Table 3): a sigmoidal type due to its high flexibility (non-linear, continuous, and differentiable functions). The results of using a linear function were significantly worse.  
The ANN learning process consisted of repeated patterns and weight modification until the network reached its target MSE after no more than 1000 epochs (Figure 3).  
The best results were obtained for the network structure parameters: n = 28, m = 4, and 35 neurons in the hidden layer (i.e., MLP 28-35-4). The ANN was able to minimize the MSE for the training set data to very low values (ranging from 0.001-0.02) (Table 4, Table 5, best choice in bold).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**ANN Structure** & \
\begin{tabular}{c} **Activation Function** \\ **in the Hidden Layer** \\ \end{tabular} &
\begin{tabular}{c} **Activation Function** \\ **in the Output Layer** \\ \end{tabular} \\ \hline MLP 28-25-4 & Sigmoid & Sigmoid \\ \hline MLP 28-30-4 & Sigmoid & Sigmoid \\ \hline
**MLP 28-35-4** & **Sigmoid** & Sigmoid \\ \hline MLP 28-40-4 & Sigmoid & Sigmoid \\ \hline MLP 28-45-4 & Sigmoid & Sigmoid \\ \hline \hline \end{tabular}
\end{table}
Table 3: The best MLP network models for diagnostic measures.  
Figure 3: Values of mean square error (MSE) during learning.  
ANN-based optimization is aimed at a relatively simple and rapid solution to the optimization problem, avoiding the definition of rules (e.g., in the absence of their knowledge), relying only on the input and output variables (data-driven approach). The proper selection of training data (sets of input and output parameters) also allows for the identification of non-obvious or previously unknown rules. This is useful for jobs with unchecked databases.  
A neural network is used to assess companies in terms of their environmental performance. There are 28 questions in the input, the answers to which are entered, and then 4 percentages of the company's environmental performance in the areas of product design, product manufacturing, product use, and product recycling are obtained as the result.  
A modified five-point Likert scale [76] was used to assess the environmental performance of the surveyed enterprises. A company can obtain one of five levels of environmental performance: definitely not ecological; rather not ecological; neutral; rather ecological; and definitely ecological. Table 6 presents a detailed analysis of the results.  
Using this scale when analyzing the percentage values obtained at the output of the neural network, it is immediately apparent in which area the company's environmental performance needs to be improved. From these four values, an average can be formulated and an overall assessment of the company's environmental performance can be determined.