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Author Spotlight: Enhancing Engineering Education via WebVR-Based Online Laboratories
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Compiling and using input-output frameworks through collaborative virtual laboratories.

Manfred Lenzen1, Arne Geschke1, Thomas Wiedmann2

  • 1ISA, School of Physics A28, The University of Sydney, NSW 2006, Australia.

The Science of the Total Environment
|April 15, 2014
PubMed
Summary
This summary is machine-generated.

The Australian Industrial Ecology Virtual Laboratory (IELab) streamlines environmental and economic data integration using a novel "mother-daughter" approach and cloud computing. This collaborative platform enhances multi-region input-output (MRIO) model accessibility and customisation for diverse research needs.

Keywords:
AustraliaCollaborationElectronic research infrastructureLife cycle assessmentMulti-region input–output tablesVirtual laboratories

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Area of Science:

  • Environmental Science
  • Economics
  • Computational Science

Background:

  • Compiling large-scale environmental and economic databases is labor- and cost-intensive due to data fragmentation and misalignment.
  • Existing multi-region input-output (MRIO) models often require fixed structures, limiting their adaptability for specific research questions.

Purpose of the Study:

  • To introduce the Australian Industrial Ecology Virtual Laboratory (IELab) as a novel, collaborative platform for creating and utilizing large-scale environmentally extended MRIO models.
  • To enhance the flexibility and accessibility of MRIO analysis through innovative data structuring and automated workflows.

Main Methods:

  • Development of the "mother-daughter" construction principle, allowing for a highly detailed "mother" table from which customized "daughter" tables are derived.
  • Introduction of a "root classification" tier enabling users to define their own mother-MRIO configurations without increased data handling costs.
  • Implementation of a highly automated collaborative research platform within a cloud-computing environment to expedite workflows.

Main Results:

  • The IELab facilitates the creation of user-defined, customized MRIO tables that maximize disaggregation relevant to specific research questions.
  • The cloud-based platform and automation significantly reduce the time and cost associated with compiling and harmonizing disparate environmental and economic data.
  • The system enables high-performance computing for MRIO analysis, making it more accessible and useful for a wider range of research applications.

Conclusions:

  • The IELab's collaborative approach, combined with its flexible data structure and automated platform, overcomes traditional barriers in environmental-economic data integration.
  • It significantly enhances the utility and accessibility of MRIO analysis for diverse research applications, promoting data harmonization and collaborative research.
  • The adoption of common analytical tools within IELab facilitates the integration of fragmented data, benefiting all stakeholders in environmental and economic research.