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This paper examines the move into upstream and downstream global value chain (GVC) activities by 11 Asian countries. We use international input–output tables in combination with employment data and measure the number of workers in each country involved in manufactures GVC. Jobs are classified by business function based on occupational information, such as R&D, fabrication, logistics, sales and marketing. In most Asian countries, we find a faster employment increase in R&D and other support services relative to fabrication activities between 2000 and 2011. However, the participation in GVC and the pace of upgrading appears to differ substantially across Asian countries. We use a structural decomposition method to explore the role of trade, consumption and technological change in accounting for changes in countries' involvement in GVC. [ABSTRACT FROM AUTHOR]

We describe the creation of the Global Multi-Region Input–Output (MRIO) Lab, which is a cloud-computing platform offering a collaborative research environment through which participants can use each other’s resources to assemble their own individual MRIO versions. The Global MRIO Lab’s main purpose is to harness and focus previously disparate resources aimed at compiling large-scale MRIO databases that provide comprehensive representations of interregional trade, economic structure, industrial interdependence, as well as environmental and social impact. Based on the operational Australian Industrial Ecology Lab, a particularly important feature of this cloud environment is a highly detailed regional and sectoral taxonomy called the ‘root classification’. The purpose of this root is to serve as a feedstock from which researchers can choose any combination of regions and economic sectors to form a model of the economy that is suitable to address their particular research questions. Thus, the Global MRIO Lab concept enables enhanced flexibility in MRIO database construction whilst at the same time saving resources and avoiding duplication, by sharing time- and labour-intensive tasks amongst multiple research teams. We explain the concept, architecture, development and preliminary results of the Global MRIO Lab, and discuss its ability to continuously deliver some of the most prominent world MRIO databases. [ABSTRACT FROM PUBLISHER]

We explore options to replicate the EXIOBASE2 multi-region input–output (MRIO) database in the Virtual IELab cloud-computing laboratory environment. Whereas EXIOBASE2 is constructed using a multi-process reconciliation procedure, we present an alternative compilation technique that uses EXIOBASE2's pre-processed data and final tables in reconciling the IELab MRIO with conflicting raw data information. This approach skips the labour-intensive step of detailing and harmonising country tables. Adherence metrics reveal the EXIOBASE2-based IELab table to be considerably less balanced than the original but with stronger adherence to other constraints data. However, these metrics are not comparable to the original EXIOBASE2 statistics due to the distinctive implementation of constraint sets in the two platforms. IELab's main value-added is its flexibility in tailoring EXIOBASE2-based MRIOs beyond the original recipe. Finally, IELab's global carbon, water and material footprints are shown to be comparable with previously reported resource footprints. In contrast, deviations in land footprints warrant further investigation. [ABSTRACT FROM PUBLISHER]

As a large archipelago with significant geographical variation and economic diversity, Indonesia requires detailed regional information when subjected to economic modelling. While such information is available, it however has not been integrated and harmonised into a comprehensive input–output database, thus preventing economic, social, and environmental modelling for investigating sub-national regional policy questions. We present the newIndoLab, a collaborative research platform for Indonesia, enabling input–output modelling of economic, social, and environmental issues in a cloud-computing environment. Within the IndoLab researchers are for the first time able to generate a time series of regionally and sectorally detailed and comprehensive, sub-national multi-region input–output (MRIO) tables for Indonesia. By integrating a multitude of economic, social, and environmental data into a single standardised processing pipeline and harmonised data repository, the IndoLab is able to generate MRIO tables capturing up to 1148 sectors, and 495 cities and regencies. Researchers can freely choose from this detail to construct tables with customised classifications that suit their own research questions. First results from the IndoLab clearly demonstrate the unique characteristics of regions in terms of their sectors’ employment intensity. Thus, the IndoLab has great potential for investigating policy questions that cannot be comprehensively addressed using a single national database. [ABSTRACT FROM PUBLISHER]

The Industrial Ecology Virtual Laboratory (IELab) is a collaborative cloud-computing platform for compiling large-scale, high-resolution, enviro-socio-economic accounts based on multi-region input–output tables and for conducting integrated sustainability assessment projects. These include, for example, assessments of biofuels and low-carbon construction materials or high-resolution waste modelling. This contribution provides a structured review of IELab applications that were published in either peer-reviewed journal papers or in the form of conference proceedings. The main research question posed is ‘What are the specific features of IELab that were used in the research and could the research have happened without them?’ It is investigated whether the IELab has actually and truly enabled new research. A detailed analysis of IELab characteristics and their usage is presented. The results can help with the design of new research projects and inform existing and prospective users of the IELab about the options for academic research and practical applications. [ABSTRACT FROM PUBLISHER]

Abstract: Compiling, deploying and utilising large-scale databases that integrate environmental and economic data have traditionally been labour- and cost-intensive processes, hindered by the large amount of disparate and misaligned data that must be collected and harmonised. The Australian Industrial Ecology Virtual Laboratory (IELab) is a novel, collaborative approach to compiling large-scale environmentally extended multi-region input–output (MRIO) models. The utility of the IELab product is greatly enhanced by avoiding the need to lock in an MRIO structure at the time the MRIO system is developed. The IELab advances the idea of the “mother–daughter” construction principle, whereby a regionally and sectorally very detailed “mother” table is set up, from which “daughter” tables are derived to suit specific research questions. By introducing a third tier – the “root classification” – IELab users are able to define their own mother-MRIO configuration, at no additional cost in terms of data handling. Customised mother-MRIOs can then be built, which maximise disaggregation in aspects that are useful to a family of research questions. The second innovation in the IELab system is to provide a highly automated collaborative research platform in a cloud-computing environment, greatly expediting workflows and making these computational benefits accessible to all users. Combining these two aspects realises many benefits. The collaborative nature of the IELab development project allows significant savings in resources. Timely deployment is possible by coupling automation procedures with the comprehensive input from multiple teams. User-defined MRIO tables, coupled with high performance computing, mean that MRIO analysis will be useful and accessible for a great many more research applications than would otherwise be possible. By ensuring that a common set of analytical tools such as for hybrid life-cycle assessment is adopted, the IELab will facilitate the harmonisation of fragmented, dispersed and misaligned raw data for the benefit of all interested parties. [Copyright &y& Elsevier]