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Large-Scale Computational Modeling for Environmental Impact Assessment
Abstract
Input-output table plays a central role in the Economic Input-Output Life Cycle Assessment (EIO-LCA) method. This chapter presents an integrated and distributed computational modeling system capable of estimating and updating large-size input-output tables. The complexity of national economy leads to extremely large-size models to represent every detail of an economy. In order to construct the table reflecting the underlying industry structure faithfully, multiple sources of data are integrated and analyzed together. The major bottleneck of matrix estimation is the lack of memory allocation. In order to include more memory, this unique distributed matrix estimation system runs over a parallel supercomputer to enable it to estimate a matrix with the size of more than 1,000-by-1,000 with relatively high accuracy. This system is the first distributed matrix estimation package for such a large-size economic matrix. This chapter presents a comprehensive example of facilitating this estimation process by integrating a series of components with the purposes of data retrieval, data integration, distributed machine learning, and quality checking.
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