2016
DOI: 10.1016/j.rser.2015.09.090
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Optimal embodied energy abatement strategy for Beijing economy: Based on a three-scale input-output analysis

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Cited by 84 publications
(30 citation statements)
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“…Unfortunately, China's city-level water-energy nexus is still poorly understood and the issues remain unsolved despite the fact that cities are the basic units for local governments to implement policies as the center for population and human activities, and that how a city obtains, distributes and manages its water and energy supply directly affects its growth [15]. Moreover, relevant studies are rather limited due to the lack of city-level water and energy statistics and of city-level input-output tables.…”
Section: Introductionmentioning
confidence: 99%
“…Unfortunately, China's city-level water-energy nexus is still poorly understood and the issues remain unsolved despite the fact that cities are the basic units for local governments to implement policies as the center for population and human activities, and that how a city obtains, distributes and manages its water and energy supply directly affects its growth [15]. Moreover, relevant studies are rather limited due to the lack of city-level water and energy statistics and of city-level input-output tables.…”
Section: Introductionmentioning
confidence: 99%
“…An input-output-based, three-scale model was established to decompose a city's footprint into flows induced by local production, domestic input, and international import (Chen et al, 2013;Li et al, 2016;Shao et al, 2016). Other studies applied a similar logic by connecting an urban input-output model with a global multiregion input-output model (MRIO) but with higher resolution regarding domestic and foreign regions (Hu et al, 2016;Lin et al, 2015;Wiedmann, 2017;Wiedmann et al, 2016).…”
Section: Introductionmentioning
confidence: 99%
“…More complete descriptions of the input-output models are presented in other existing input-output analysis related works (Peters et al, 2006;Miller and Blair, 2009;Chen and Zhang, 2010;Zhang and Chen, 2010;Li et al, 2016).…”
Section: Algorithmmentioning
confidence: 99%