Assessment of clean production level in phosphate mining enterprises: Based on the fusion group decision weight and limited interval cloud model

To develop a robust cleaner production level assessment model for phosphate mining enterprises, this study investigated the key factors influencing their cleaner production levels. The model incorporates integrated group decision weights and a limited interval cloud model. Based on the comprehensive life cycle of phosphate mining production, we constructed a detailed evaluation index system to assess the cleaner production level of these enterprises. This system defined the quantitative ranges for each level and revealed the interrelationships among the indicators. To ensure comprehensive weight determination, we proposed a method that integrates the expertise of multiple stakeholders. This method leveraged group decision-making principles to effectively resolve conflicts in expert judgments during the weight determination process. In addressing the characteristics of evaluation indicators for the cleaner production level of phosphate mining enterprises, we proposed employing a finite interval cloud model to accurately characterize the distribution of the indicators. Leveraging the finite interval cloud model, we obtained numerical characteristics for various indicators and incorporated the weights of the integrated group decision-making attributes. This approach enabled us to determine the cleaner production level of phosphate mining using a finite interval cloud generator. Validation using five phosphate mining enterprises in Hubei Province, China, confirmed the model's high accuracy. This model demonstrated its applicability in evaluating cleaner production levels for phosphate mining enterprises, offering a theoretical foundation for developing targeted environmental management policies. •Reasonable grading standards form the basis of cleaner production level assessment.•The distribution of indicator grades can be characterized using finite intervals.•The assessment of cleaner production levels involves fuzziness and uncertainty.•Aggregating group decision attribute weights can improve model accuracy.