Developing an Innovative Regression Model to Predict Industrial Facilities’ Risk Scores by Leveraging Environmental Compliance Assessments Data

Environmental regulatory authorities are crucial in formulating and enforcing regulations to protect the environment and public health. The current practices involve field inspections, compliance monitoring, and hazard evaluations to identify and prioritize risky facilities. However, implementing these practices can be challenging, especially due to the time required for regular hazard evaluations. Environmental regulators implement the Risk-Based Inspection (RBI) Program to address this challenge. This program optimizes inspection resources by targeting higher-risk facilities through a systematic approach integrating quantitative risk modeling, subjective performance assessment, and risk-related information. The RBI Program aims to refine inspection methods, minimize unnecessary efforts and costs, transition to proactive compliance monitoring, and establish an effective inspection program. This research highlights the intrinsic connection between the facility’s compliance and overall risk, emphasizing their close alignment. Compliance, encompassing adherence to mandatory requisites and voluntary commitments, is crucial in safeguarding facilities from risks that could lead to non-compliance. The study aims to correlate the total number of violations from compliance assessment inspection visits with risk scores derived from hazard evaluations in concrete batching facilities, which are major components of the cement and cement products industrial sector. The study also identified a smart tool that can help in efficient resource allocation and maintaining the facilities’ rank updated. This tool is a systematically developed linear regression model that can be used as a foundation to predict the risk scores from compliance data for facilities in various industrial sectors. This widely used statistical model enables environmental regulators to optimize resource allocation, reduce monitoring and inspection costs, and prioritize facilities with significant environmental impact. The study presents a fundamental scalable model as a machine-learning method that can be efficiently used for inspection allocations, prioritizing visits based on the risk category of facilities. Overall, this research contributes to the advancement of environmental inspection and compliance practices and can be applied to various industrial sectors.