Exploring the multidimensional factors and emergence mechanisms of industrial symbiotic relationships based on machine learning. (25th December 2022)

• Record Type: Journal Article
• Authors: Wang, Shida; Zhang, Zimeng; Wang, Zhen; Liu, Gang
• Is Part Of: Journal of cleaner production. Volume 381:Part 1(2022)
• Publication Date: 2022-12-25
• Subjects: Symbiosis relationship -- Industrial symbiosis -- Influencing factors -- Emergence mechanism -- Extreme gradient boosting (XGBoost) -- China; Factory and trade waste -- Management -- Periodicals; Manufactures -- Environmental aspects -- Periodicals; Déchets industriels -- Gestion -- Périodiques; Usines -- Aspect de l'environnement -- Périodiques; 628.5
• DOI: 10.1016/j.jclepro.2022.135169
• Languages: English
• ISSNs: 0959-6526

Abstract

Abstract: Industrial symbiosis is widely sought for industrial sustainability in recent years. While multiple factors are identified important for industrial symbiosis occurrence, the complex interactions among these factors and the emergence mechanisms of industrial symbiotic relationships (SR) remain not well explored in the literature, especially not with empirical data. In this study, we aim to address this knowledge gap based on machine learning and first-hand data collected from a survey of 201 enterprises in Chun'an County, Zhejiang Province, China. The mechanisms behind these symbiotic relationships were explored by simulating the interactions and nonlinear effects of 37 selected factors categorized into sociopolitical, economic, and technological dimensions. We found that the sociopolitical dimension factors (particularly the enterprises' demand for cleaner production and the indirect influence from similar environmental protection behaviors of other enterprises) play the most important role behind industrial SR establishment. Imperfect regulations or lack of legal basis, the scale of economy for SR participation, and the varying quality of reused raw materials or products are identified as key obstacles. Our multidimensional analysis revealed non-linear effects suggests that such a system understanding on influencing factors and the emergence mechanism of industrial SR is necessary and important to formulate relevant policy for boosting industrial symbiosis and thus industrial sustainability. Graphical abstract: Image 1 Highlights: We explored the sociopolitical, economic, and technological factors of influencing industrial symbiosis. An empirical analysis in China based on the primary data collected from 201 enterprises. The sociopolitical dimension factors play the most important roles in the occurrence of symbiotic relationship. A machine-learning based multidimensional analysis revealed non-linear effects of the complex interactions among factors.