Constructing enterprise input-output tables - a case study of New Zealand dairy products

Lenzen, Manfred; Lundie, Sven

doi:10.1186/2193-2409-1-6

Cited by 11 publications

(7 citation statements)

References 25 publications

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“…There is plenty of literature on RAS and other RAS-based variations (e.g. Junius and Oosterhaven, 2003 (GRAS); Lenzen et al, 2007 (KRAS); Huang et al, 2008 (IGRAS); Lenzen and Lundie, 2012;and Temurshoev, Miller and Bouwmeester, 2013) but they all balance matrices with the same number of rows and columns. Note, instead, that the number of rows and columns of the original (A) and target (E) matrices in the RACE conversion method are not necessarily the same.…”

Section: Race: a New Approachmentioning

confidence: 99%

Can supply, use and input–output tables be converted to a different classification with aggregate information?

Rueda‐Cantuche

Amores

Remond-Tiedrez

2019

Economic Systems Research

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Every change in the product and/or industry classifications and/or methodology of supply, use and input-output tables makes any medium-to long-term policy analysis impossible unless appropriate conversions are provided by national statistical institutes using more detailed data. However, can these tables be reasonably converted to a different classification of industries and products using aggregate information? We develop a conversion method that allows changes in classification that are independent of the number of industries and products. In addition, we provide evidence about its empirical performance compared with projection methods. We find projection methods perform better than conversion methods, at least when using aggregate information. Nonetheless, unlike conversion methods, projection methods generally require supply, use and input-output tables in the new classification that might not always be available. In their absence, we recommend using more detailed and sophisticated data.

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Section: Race: a New Approachmentioning

confidence: 99%

Can supply, use and input–output tables be converted to a different classification with aggregate information?

Rueda‐Cantuche

Amores

Remond-Tiedrez

2019

Economic Systems Research

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“…How can a seed matrix that is both informative and feasible be obtained in an applied context? Work on the construction of semi-survey enterprise input-output tables by Lenzen and Lundie (2012) suggests that, in the absence of more precise prior information, a fairly sparse non-negative matrix can still be recovered to a reasonable degree of accuracy by initializing RAS with a binary matrix that identifies which elements of the estimated are believed to be nonzero. In our context, a correctly specified binary seed in the spirit of Lenzen and Lundie (2012) would be given by an m 1 × m 2 matrix, D b , whose generic element d b ij is one if source industry i and target industry j have at least one fundamental product in common, and zero otherwise.…”

Section: Seed Matrix Specificationmentioning

confidence: 99%

“…Secondly, we contrast the performance of count-seed RAS with that of binary-seed RAS. In this way, we aim to evaluate to what extent, if at all, reclassification accuracy is improved by adopting the seed matrix specification D put forward in this paper instead of a binary prior in the spirit of Lenzen and Lundie (2012), such as D b .…”

Section: Validationmentioning

confidence: 99%

“…What is challenging about the specific RAS application discussed here is that it is not obvious how to construct a plausible seed matrix. In the spirit of and Lenzen and Lundie (2012), a simple option would be to use a binary seed matrix based on a readily available qualitative table of correspondences between classifications. All data reclassifications in Cai (2016), for example, took this approach.…”

Section: Introductionmentioning

confidence: 99%

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Bridging macroeconomic data between statistical classifications: the count-seed RAS approach

Cai

Rueda‐Cantuche

2018

Economic Systems Research

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In applications, it is often necessary to link heavily aggregated macroeconomic datasets adhering to different statistical classifications. We propose a simple data reclassification procedure for those cases in which a bridge matrix grounded in microdata is not available. The essential requirement of our approach, which we refer to as count-seed RAS, is that there exists a time period or a geographical entity similar to the one of interest for which the relevant economic variable is observed according to both classifications. From this information, a bridge matrix is constructed using bi-proportional methods to rescale a seed matrix based on a qualitative correspondence table from official sources. We test the procedure in two case studies and by Monte Carlo methods. We find that, in terms of reclassification accuracy, it performs noticeably better than other expeditious methods. The analytical framework underlying our approach may prove a useful way of conceptualizing data reclassification problems.

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“…regional planning [179]; analysis of a specific sector [179], [180]; and study of enterprise's economy [181][182][183]). Some extensions and combination of IO models with other methods have been developed in order to extend their application to different topics and allowed modelling complex systems regarding economic and physical relations [184][185][186].…”

Section: Input-output Multi-objective Models To Assess Economic-energmentioning

confidence: 99%

The challenging paradigm of interrelated energy systems towards a more sustainable future

Soares

Martins

Carvalho³

et al. 2018

Renewable and Sustainable Energy Reviews

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This paper brings together several contemporary topics in energy systems aiming to provide a literature review based reflection on how several interrelated energy systems can contribute together to a more sustainable world. Some directions are discussed, such as the improvement of the energy efficiency and environmental performance of the systems, the development of new technologies, the increase of the use of renewable energy sources, the promotion of holistic and multidisciplinary studies, and the implementation of new management rules and "eco-friendly and sustainable" oriented policies at different scales. The interrelations of the diverse energy systems are also discussed in order to address their main social-economic-environmental impacts. The subjects covered include the assessment of the electricity market and its main players (demand, supply, distribution), the evaluation of some urban systems (buildings, transportation, commuting), the analysis of the implementation of renewable energy cooperatives, the discussion of the diffusion of the electric vehicle and the importance of new bioenergy systems. This paper also presents relevant research carried out in the framework of both the Energy for Sustainability Initiative of the University of Coimbra and the Sustainable Energy Systems focus area of the MIT-Portugal Program. To conclude, several research topics that should be addressed in the near future are proposed.

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Constructing enterprise input-output tables - a case study of New Zealand dairy products

Cited by 11 publications

References 25 publications

Can supply, use and input–output tables be converted to a different classification with aggregate information?

Can supply, use and input–output tables be converted to a different classification with aggregate information?

Bridging macroeconomic data between statistical classifications: the count-seed RAS approach

The challenging paradigm of interrelated energy systems towards a more sustainable future

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