Abstract
Known methods for sustainability enhancement are typically scenario based, and the uncertainty of surrounding available data and information is usually not addressed holistically, due to inherent problem complexity. Thus the solutions identified by those methods could be not sufficiently effective in many industrial applications. In this paper, we introduce a Monte Carlo-based simulation and system optimization method for deriving sustainability enhancement strategies, where uncertainties are systematically taken into account. The methodological efficacy is illustrated through the study of an industrial sustainability enhancement problem involving a number of sectors.
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This work was supported in part by the National Science Foundation Grants (No. 1140000, 1322172, and 1434277).
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Liu, Z., Huang, Y. Sustainability enhancement under uncertainty: a Monte Carlo-based simulation and system optimization method. Clean Techn Environ Policy 17, 1757–1768 (2015). https://doi.org/10.1007/s10098-015-0916-y
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DOI: https://doi.org/10.1007/s10098-015-0916-y