Incorporating data quality improvement into supply–use table balancing

Serpell, Martin

doi:10.1080/09535314.2017.1396962

Cited by 6 publications

(4 citation statements)

References 19 publications

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“…The methods introduced in this article do not deal with issues such as managing potential conflicting information and reliability aspects of the exogenous information provided, nor other features such as non-unity coefficients in the constraints. Some alternatives that address these issues are the KRAS 2 method (Lenzen et al, 2009), the CFB algorithm of Dalgaard and Gysting (2004) and the most recent proposal of boundary tightening algorithm developed by Serpell (2018).…”

Section: Joint Estimation Of Sutsmentioning

confidence: 99%

Projecting supply and use tables: new variants and fair comparisons

Jaramillo

Rueda‐Cantuche

Olmedo

et al. 2018

Economic Systems Research

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We have introduced in this paper new variants of two methods for projecting Supply and Use Tables that are based on a distance minimisation approach (SUT-RAS) and the Leontief model (SUT-EURO). We have also compared them under similar and comparable exogenous information, i.e.: with and without exogenous industry output, and with explicit consideration of taxes less subsidies on products. We have conducted an empirical assessment of all of these methods against a set of annual tables between 2000 and 2005 for Austria, Belgium, Spain and Italy. From the empirical assessment, we obtained three main conclusions: (a) the use of extra information (i.e. industry output) generally improves projected estimates in both methods; (b) whenever industry output is available, the SUT-RAS method should be used and otherwise the SUT-EURO should be used instead; and (c) the total industry output is best estimated by the SUT-EURO method when this is not available.

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Section: Joint Estimation Of Sutsmentioning

confidence: 99%

Projecting supply and use tables: new variants and fair comparisons

Jaramillo

Rueda‐Cantuche

Olmedo

et al. 2018

Economic Systems Research

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“…However, from this step, the tables can be used with the standard IO theory to generate balanced tables and perform further analysis. Many balancing approaches already exist in the literature (Nicolardi, 2013; Serpell, 2018; Stanger, 2018). Balancing approach in our bottom‐up approach is briefly summarized in Section 3.…”

Section: Automating Piot Generation Via Mfdes Tool: Architecture Info...mentioning

confidence: 99%

PIOT‐Hub ‐ A collaborative cloud tool for generation of physical input–output tables using mechanistic engineering models

Vunnava

Shin

Zhao

et al. 2021

J of Industrial Ecology

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Mapping material flows in an economy is crucial to identifying strategies for resource management toward lowering the waste and environmental impacts of society, a key objective of research in industrial ecology. However, constructing models for mapping material flows at a sectoral level, such as in physical input–output tables (PIOTs) at highly disaggregated levels, is tedious and relies on a large amount of empirical data. To overcome this challenge, a novel collaborative cloud platform PIOT‐Hub is developed in this work. This platform utilizes a Python‐based simulation system for extracting material flow data from mechanistic models, thus semi‐automating the generation of PIOTs. The simulation system implements a bottom‐up approach of utilizing scaled engineering models to generate physical supply tables (PSTs) and physical use tables (PUTs) which are converted to PIOTs (described in (Vunnava & Singh, 2021)). Mechanistic models can be uploaded by users for sectors on PIOT‐Hub to develop PIOTs for any region. Both models and resulting PST/PUT/PIOTs can be shared with other users utilizing the collaborative platform. The automation and sharing features provided by PIOT‐Hub will help to significantly reduce the time required to develop PIOT and improve the reproducibility/continuity of PIOT generation, thus allowing the study of the changing nature of material flows in regional economy. In this paper, we describe the simulation system MFDES and PIOT‐Hub architecture/functionality through a demo example for creating PIOT in agro‐based sectors for Illinois. Future work includes scaling up the cloud infrastructure for large scale PIOT generation and enhancing the tool compatibility for different sectors in economy.

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“…It is easy to see why: the systems obtained by each pure technology assumption are balanced by default, and the hybrid will be linear combination thereof, which is also balanced. In the empirical application we do not implement any balancing operation (Lahr and de Mesnard, 2004;Rodrigues, 2014;Serpell, 2018); this avoids introducing spurious information to the assumption selection process.…”

Section: Industry-product Hybrid Technologymentioning

confidence: 99%

Bayesian selection of technology assumptions for the transformation from supply-use to input–output tables

Rodrigues

Amores

Paulo

2019

Economic Systems Research

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In the construction of input-output models from supply-use tables, technology assumptions disambiguate how an industry uses inputs in the production recipe of multiple outputs. This paper uses Bayes' theorem to select technology assumptions, taking into account empirical observations. The paper presents a formulation to explore hybrids between product and industry technology assumptions in product-by-product tables. We then present Markov chain Monte-Carlo techniques to implement the Bayesian method for selecting technology assumptions. We apply the method in a case study using Eurostat supply-use tables of 2004 and 2005, exhibiting a volume of secondary products of less than 13%, and 59 products and industries per country. The results show that the choice of technology is not important, given that there is no strong evidence in favour of any of them.

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Incorporating data quality improvement into supply–use table balancing

Cited by 6 publications

References 19 publications

Projecting supply and use tables: new variants and fair comparisons

Projecting supply and use tables: new variants and fair comparisons

PIOT‐Hub ‐ A collaborative cloud tool for generation of physical input–output tables using mechanistic engineering models

Bayesian selection of technology assumptions for the transformation from supply-use to input–output tables

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