Prediction of implementing ISO 14031 guidelines using a multilayer perceptron neural network approach.

Area of Science:

• Environmental Management Systems
• Industrial Ecology
• Information Technology in Environmental Science

Background:

• ISO 14031 provides guidelines for environmental performance evaluation.
• ISO 14001 specifies requirements for an environmental management system.
• The integration of these standards can enhance environmental performance and certification processes.

Purpose of the Study:

• To model the relationship between ISO 14031 implementation and ISO 14001 certification.
• To assess the predictive capability of ISO 14031 guidelines on ISO 14001 certification status.
• To develop a neural network model for predicting ISO 14001 certification.

Main Methods:

• Quantitative approach utilizing a survey of 596 organizations across 30 economic activities in Saudi Arabia.
• Development and training of a multi-layered neural network to analyze the relationship between ISO 14031 and ISO 14001.
• Testing the predictive accuracy of the neural network on a separate dataset.

Main Results:

• The neural network achieved 94.00% accuracy in classifying ISO 14001 certification status on the training sample.
• The model demonstrated 91.00% prediction accuracy on the test sample.
• An overall prediction efficiency of 91.30% was achieved, validating the model's effectiveness.

Conclusions:

• The study successfully modeled the link between ISO 14031 and ISO 14001 certification.
• The developed neural network model offers a robust tool for predicting environmental certification.
• Findings support the integration of ISO 14031 and ISO 14001 into a unified standard for improved environmental management.