The scale effects of symbiotic relationships under complex driving factors: An empirical study in China.
Symbiotic relationships between enterprises help mitigate resource and environmental impacts of industrial activities via exchanging waste or by‐products as material inputs among each other. However, the emergence of such symbiotic relationships under complex driving factors across different geograp...
| Publicado en: | Journal of Industrial Ecology Vol. 28; no. 6; pp. 1979 - 1996 |
|---|---|
| Autores principales: | , , , , , |
| Formato: | Artículo |
| Publicado: |
Springer Nature
Dec2024
|
| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=181847506&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 181847506 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 10881980 FL1 jtl: Journal of Industrial Ecology issn: 10881980 maglogo: N pubinfo: dt: Dec2024 vid: 28 iid: 6 pid: 237 pub: Springer Nature artinfo: ui: 181847506 10.1111/jiec.13583 ppf: 1979 ppct: 17 formats: tig: atl: The scale effects of symbiotic relationships under complex driving factors: An empirical study in China. aug: au: Chen, Hongjia Zhang, Zimeng Ioppolo, Giuseppe Shi, Lei Wang, Zhen Liu, Gang affil: College of Environmental Science and Engineering, Beijing Forestry University, Beijing, China Department of Economics, University of Messina, Messina, Italy School of Resources & Environment, Nanchang University, Nanchang, China College of Urban and Environmental Sciences, Peking University, Beijing, China SDU Life Cycle Engineering, Department of Green Technology, University of Southern Denmark, Odense, Denmark su: Industrial ecology Empirical research Industrial capacity Random forest algorithms Machine learning sug: subj: Industrial ecology Empirical research Industrial capacity Random forest algorithms Machine learning keyword: driving factors emergence mechanism geographic scales industrial symbiosis machine learning Shannon index symbiotic relationships driving factors emergence mechanism geographic scales industrial symbiosis machine learning Shannon index symbiotic relationships ab: Symbiotic relationships between enterprises help mitigate resource and environmental impacts of industrial activities via exchanging waste or by‐products as material inputs among each other. However, the emergence of such symbiotic relationships under complex driving factors across different geographical scales remains hitherto not well understood. Here, we provide an analytic framework including a random forest model and Shannon index, to systematically describe and explain the scale effects of driving factors underlying the symbiotic relationships. Based on a questionnaire survey for 324 enterprises in Chun'an, a typical industrial city in eastern China, we applied this analytical framework. The results show that, first, the quantity of symbiotic relationships exhibits an inversely proportional function across various geographical scales. Second, there exist significant differences in the dominant factors at different scales. Finally, the diversity of importance of factors and the emergence of symbiotic relationships exhibit a consistent trend of fluctuation, providing evidence for the explanatory potential of our proposed analytical framework for the driving mechanisms of emergence. We find that when enterprises are simultaneously affected by multiple driving factors with potent forces (referred to as the diversity of importance), symbiotic behaviors are more likely to occur. Moreover, our results suggest that fostering symbiotic relationships necessitates considering the variations in driving factors across different scales comprehensively and formulating targeted promotional measures tailored to the specific driving factors of different enterprise types. Our proposed framework would help to maximize industrial symbiosis potentials in a specific region. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
|---|