Projection of Supply and Use Tables: Methods and Their Empirical Assessment

Temursho, Umed; Yamano, Norihiko; Webb, Colin

doi:10.1080/09535314.2010.534978

Cited by 53 publications

(21 citation statements)

References 35 publications

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“…20 The change in sign is a potential problem when using methods with quadratic or higher order objective functions. One way to deal with this issue is to nullify sign-changing elements in the optimization process by introducing an appropriately defined penalty function in the considered objective function (see, e.g., Temurshoev, Webb, & Yamano, 2011). Otherwise, one can simply use the nonnegativity constraints.…”

Section: Empirical Simulationsmentioning

confidence: 99%

Entropy‐based benchmarking methods

Temursho

2018

Statistica Neerlandica

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This paper argues that benchmarking of sign‐volatile time series has to be based on the principle of movement and sign preservation, which requires the benchmarked series to reproduce both the short‐term movements and signs of the original series. It is shown that the widely used variants of Denton method and the Causey‐Trager growth rate preservation method may violate this principle. Four variants of entropy‐based benchmarking methods are proposed that, by construction, account for the requirements of the movement and sign preservation principle. Our extensive simulations confirm that the proposed entropy methods can be regarded as plausible competitors for the well‐established benchmarking methods and may even be preferred in cases of benchmarking series that are highly volatile in sign and/or movements.

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Section: Empirical Simulationsmentioning

confidence: 99%

Entropy‐based benchmarking methods

Temursho

2018

Statistica Neerlandica

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“…The implementation of a root-mother-daughter approach for large-scale MRIO construction is associated with a range of challenges. Generally speaking, constructing an MRIO table involves (a) building an initial estimate and a set of constraints; and then (b) combining these in a mathematical optimisation operation to yield a final MRIO table that satisfies the conditions posed by the constraints in an optimal way (Temurshoev et al 2011). The strategy in Wittwer and Horridge's TERM approach is to generate CGE simulation tablespost-optimisation -from a fixed mother (master) table.…”

Section: The Root-mother-daughter Approach To Compiling Large-scale Mmentioning

confidence: 99%

“…Building a fully populated MRIO mother at this detail would clearly be impossible since processing the required square 2.8million2.8million-sized matrix would exceed current computational capacities. However, the flexibility of deriving a large variety of daughter tables can be achieved before actually building a mother table, by building flexibility into the mother's construction algorithms.Generally speaking, constructing an MRIO table ("MRIO" in short) involves building an initial estimate (IE) and a set of constraints, which are then combined in a mathematical optimisation operation to yield a final MRIO table that satisfies the conditions posed by the constraints in an optimal way(Temurshoev et al 2011). In essence, the TERM strategy is to generate CGE simulation tables from a mother table post-optimisation.…”

mentioning

confidence: 99%

Compiling and using input–output frameworks through collaborative virtual laboratories

Lenzen

Geschke

Wiedmann

et al. 2014

Science of The Total Environment

169

138

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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.

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“…Therefore, partial‐survey methods that incorporate nonmodel information in a nonsurvey model have been advocated (Lahr, ) and widely applied in many global and regional‐level MRIO databases, such as WIOD, IDE‐JETRO MRIO, and China MRIO. The conventional steps of MRIO table construction with partial‐survey methods can be summarized as follows: first, an initial estimate matrix and constraints are built using nonsurvey methods, after which mathematical optimization operations (e.g., the RAS method) or disaggregation of multipliers are used to yield a final MRIO table or multipliers that meet the study requirements (Oosterhaven, ; Temurshoev, Webb, & Yamano, ). These steps work for regional or national MRIO compilations with reasonable workloads.…”

Section: Introductionmentioning

confidence: 99%

Linking city‐level input–output table to urban energy footprint: Construction framework and application

Zheng

Meng

et al. 2019

J of Industrial Ecology

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Multiregion input-output (MRIO) models have become increasingly important in economic and environmental analysis. However, the current resolution of most MRIO models fails to capture the heterogeneity between subregions, especially in cities. The lack of city-level MRIO tables has impeded the accomplishment of city-level studies and hampered the understanding of the relationship between urban growth and consumption, and teleconnections to other regions. In this paper, we propose a partial survey-based multiple-layer framework for MRIO table compilation of a Chinese province that distinguishes city-based regions. This framework can effectively address a large number of data processes and retain consistency between layers. Using the framework, we first compile a nested Hebei-China city-level MRIO table and then apply city-level energy footprint accounting of the North China urban agglomeration. Our results present the critical role of Hebei cities in energy supply in 2012 and quantify energy use embodied in goods for the domestic trade. Tangshan, Shijiazhuang, and Handan are distinctive cities in the energy supply chain of other regions, for both less developed and developed regions. This multiple-layer framework represents a feasible approach for developing subregional-level MRIO models and offers the possibility to analyze global trade at the subregional level with limited data.

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Projection of Supply and Use Tables: Methods and Their Empirical Assessment

Cited by 53 publications

References 35 publications

Entropy‐based benchmarking methods

Entropy‐based benchmarking methods

Compiling and using input–output frameworks through collaborative virtual laboratories

Linking city‐level input–output table to urban energy footprint: Construction framework and application

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