Provide an audit model of environmental compliance to monitor the executive organs of the country

Environmental auditing has become an important tool for maintaining and achieving environmental pollution control standards. Lack of a model for environmental compliance auditing in order to inspection of the Supreme Audit Court of Iran over the executive organs is one of the challenges of not implementing the environmental compliance auditing; therefore, the purpose of this study is to provide an audit model for environmental compliance in order to inspect the executive organs.

In this study, first, the initial indicators were identified based on the study of research literature and statistical method of qualitative content analysis. Then, in the fall of 2021, by interviewing 35 auditors of the Iran’s Supreme Audit Court as experts, who were selected by the snowball judgment method and by the Delphi statistical method the initial indicators were identified and finalized. Then, using a modern questionnaire and modern analytical methodology, structural-interpretive modeling and by MATLAB and Excel softwares, the relationships of the indicators were determined, analyzed and ISM graph were drawn. Finally, using MICMAC analysis, the influence power and dependence of the identified indicators were evaluated and the final pattern was drawn. Validity in content and reliability using Cronbach's alpha coefficient was calculated.

The environmental compliance audit model for inspecting the executive organs of Iran has indicators for national laws and regulations, transnational laws and regulations, international agreements, contracts, policies and guidelines.

Indices of national and transnational laws and regulations with high influence and low dependence are independent and effective indicators in the model. Indices of policies with high power of influence and dependence as a link index, and indicators of international agreements and contracts with low power of influence and high dependence as dependent indicators have the least impact on the model.