Urban Metabolism of Three Cities in Jing-Jin-Ji Urban Agglomeration, China: Using the MuSIASEM Approach

Wang, Xiaoyue; Wu, Shuyao; Li, Shuangcheng

doi:10.3390/su9081481

Cited by 14 publications

(2 citation statements)

References 41 publications

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“…Zhang, et al [73] Energy and carbon dioxide Chen and Chen [74] Carbon dioxide Huang and Chen [76] Emergy (seJ) Lu, et al [77] Human time and Land Use Tan, et al [78] Relationship indicators Wang, et al [79] Energy and labour Sun, et al [80] Emergy (seJ)…”

Section: Resultsmentioning

confidence: 99%

“…We identified two integrated decision support models in the case studies: Multi-Scale Integrated Analysis of Societal and Ecosystem Metabolism (MuSIASEM; Giampietro and Mayumi [14]) and LEAM [98]. Four studies we reviewed used the MuSIASEM approach [77,79,101,102] while LEAM was used only once. Studies that used MuSIASEM placed stronger emphasis on CHANS by assessing labour and wealth alongside material and energy flows, but these were not found to be relevant for assessing ES.…”

Section: Model Complexitymentioning

confidence: 99%

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Pathways to Modelling Ecosystem Services within an Urban Metabolism Framework

et al. 2019

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Urbanisation poses new and complex sustainability challenges. Socio-economic activities drive material and energy flows in cities that influence the health of ecosystems inside and outside the urban system. Recent studies suggest that these flows, under the urban metabolism (UM) metaphor, can be extended to encompass the assessment of urban ecosystem services (UES). Advancing UM approaches to assess UES may be a valuable solution to these arising sustainability challenges, which can support urban planning decisions. This paper critically reviews UM literature related to the UES concept and identifies approaches that may allow or improve the assessment of UES within UM frameworks. We selected from the UM literature 42 studies that encompass UES aspects, and analysed them on the following key investigation themes: temporal information, spatial information, system boundary aspects and cross-scale indicators. The analysis showed that UES are rarely acknowledged in UM literature, and that existing UM approaches have limited capacity to capture the complexity of spatio-temporal and multi-scale information underpinning UES, which has hampered the implementation of operational decision support systems so far. We use these results to identify and illustrate pathways towards a UM-UES modelling approach. Our review suggests that cause–effect dynamics should be integrated with the UM framework, based on spatially-specific social, economic and ecological data. System dynamics can inform on the causal relationships underpinning UES in cities and, therefore, can help moving towards a knowledge base tool to support urban planners in addressing urban challenges.

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