Balancing and reconciling large multi-regional input–output databases using parallel optimisation and high-performance computing View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2019-12

AUTHORS

Arne Geschke, Julien Ugon, Manfred Lenzen, Keiichiro Kanemoto, Daniel Dean Moran

ABSTRACT

Over the past decade, large-scale multi-regional input–output (MRIO) tables have advanced the knowledge about pressing global issues. At the same time, the data reconciliation strategies required to construct such MRIOs have vastly increased in complexity: large-scale MRIOs are more detailed and hence require large amounts of different source data which are in return often varying in quality and reliability, overlapping, and, as a result, conflicting. Current MRIO reconciliation approaches—mainly RAS-type algorithms—cannot fully address this complexity adequately, since they are either tailored to handle certain classes of constraints only, or their mathematical foundations are currently unknown. Least-squares-type approaches have been identified to offer a robust mathematical framework, but the added complexity in terms of numerical handling and computing requirements has so far prevented the use of these methods for MRIO reconciliation tasks. We present a new algorithm (ACIM) based on a weighted least-squares approach. ACIM is able to reconcile MRIO databases of equal or greater size than then currently largest global MRIO databases. ACIM can address arbitrary linear constraints and consider lower and upper bounds as well as reliability information for each given data point. ACIM is based on judicious data preprocessing, state-of-the art quadratic optimisation, and high-performance computing in combination with parallel programming. ACIM addresses all shortcomings of RAS-type MRIO reconciliation approaches. ACIM’s was tested on the Eora model, and it was able to demonstrate improved runtimes and source data adherences. More... »

PAGES

2

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Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/s40008-019-0133-7

DOI

http://dx.doi.org/10.1186/s40008-019-0133-7

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https://app.dimensions.ai/details/publication/pub.1111542657


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