Identifying critical energy-water paths and clusters within the urban agglomeration using machine learning algorithm

Energy and water shortages are two major problems in the process of urban development, meeting demands for energy fresh has become key to global sustainable development. In this study, we developed a structure-based singular value decomposition (SSVD) method through incorporating techniques multi-regional input-output (MRIO), structural path analysis (SPA), (SVD) within general framework. The SSVD is used explore track system properties flow paths energy-water nexus network Pearl River Delta agglomeration (PUA) from 2012 2015. Our main findings are: (i) largest final demand inducing energy-related (E-water) water-related (W-energy) exports; (ii) Shenzhen mainly depends on other cities E-water W-energy, Huizhou provider W-energy; (iii) identified over 10,000 clusters found that Guangzhou's electricity equipment manufacture drive clusters, respectively. suggest monitoring consumption supply chains agglomerations can provide new insights into policies.