Forecasting Replacement Demand of Durable Goods and the Induced Secondary Material Flows: A Case Study of Automobiles

Kagawa, Shigemi; Nakamura, Shinichirô; Kondo, Yasushi; Matsubae, Kazuyo; Nagasaka, Tetsuya

doi:10.1111/jiec.12184

Cited by 18 publications

(11 citation statements)

References 21 publications

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“…The general system structure is more comprehensive, however, because the final use phase with capital stocks is not included in SUTs and I‐O models. This lack of coverage of I‐O systems can be overcome by combining dynamic stock models of the use phase with WIO models (Nakamura et al ; Kagawa et al ; Pauliuk et al ).…”

Section: A General System Structure Of Socioeconomic Metabolismmentioning

confidence: 99%

A General System Structure and Accounting Framework for Socioeconomic Metabolism

Pauliuk

Majeau‐Bettez

Müller

2015

J of Industrial Ecology

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SummaryA wide spectrum of accounting frameworks and models is available to describe socioeconomic metabolism (SEM). Despite the common system of study, a large variety of terms and representations of that system are used by different models. This makes it difficult for practitioners to compare and choose a model or model combination that is fit for purpose. To facilitate model comparison, we analyze the system structure of material flow analysis (MFA); life cycle assessment (LCA); supply and use tables (SUTs); Leontief, Ghosh, and waste input-output analysis; integrated assessment models; and computable general equilibrium models. We show that the typical system structure of MFA and LCA is a directed graph. For the other models and some MFA and LCA studies, the system structure is a bipartite directed graph. We demonstrate that bipartite directed graphs and SUTs are equivalent representations of SEM. We show that the system structures of the models above are special cases of a general system structure, which models SEM as a bipartite graph. The general system structure includes industries, markets, the final use phase, products, waste, production factors, resources, and emissions. From the general system structure, we derive an accounting framework in the form of a generalized SUT. The general system structure facilitates the development of clear and unambiguous terminology across models. It helps to identify rules for the correct accounting of waste flows and stock changes. It facilitates model comparison and can serve as a blueprint for a model-independent database of SEM. Keywords:accounting bipartite graphs industrial ecology model societal metabolism supply and use tables system of national accounts (SNA) Supporting information is available on the JIE Web site

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Section: A General System Structure Of Socioeconomic Metabolismmentioning

confidence: 99%

A General System Structure and Accounting Framework for Socioeconomic Metabolism

Pauliuk

Majeau‐Bettez

Müller

2015

J of Industrial Ecology

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“…At present, there is no connection between the modules for LCA and I‐O and the dynamic_stock_model class. This will change once dynamic stock models will be used as exogenous drivers for dynamic I‐O models (Nakamura et al ; Pauliuk et al ; Kagawa et al ).…”

Section: Guidelines For Collaborative Software Projects Within Industmentioning

confidence: 99%

“…Systems containing between 1 and approximately 25 processes are often visualized with the freeware application STAN, which can also handle data reconciliation and uncertainty propagation (Vienna University of Technology ). Specialized software is indispensable for more comprehensive systems, including dynamic MFA and dynamic stock models (Hatayama et al ; Modaresi et al ; Elshkaki and Graedel ; Wiedenhofer et al ; Müller ; Daigo et al ; Busch et al ), MFA models of complex supply chains (Wiedmann et al ; Nakamura and Nakajima ; Nakamura et al ; Kagawa et al ), and quantification of stocks with high spatial resolution (Takahashi et al ). Transparency of MFA and SFA studies is enhanced by explicit system definitions that accompany many publications.…”

Section: Introductionmentioning

confidence: 99%

Lifting Industrial Ecology Modeling to a New Level of Quality and Transparency: A Call for More Transparent Publications and a Collaborative Open Source Software Framework

Pauliuk¹,

Majeau‐Bettez²,

Mutel³

et al. 2015

J of Industrial Ecology

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SummaryIndustrial ecology (IE) is a maturing scientific discipline. The field is becoming more data and computation intensive, which requires IE researchers to develop scientific software to tackle novel research questions. We review the current state of software programming and use in our field and find challenges regarding transparency, reproducibility, reusability, and ease of collaboration. Our response to that problem is fourfold: First, we propose how existing general principles for the development of good scientific software could be implemented in IE and related fields. Second, we argue that collaborating on open source software could make IE research more productive and increase its quality, and we present guidelines for the development and distribution of such software. Third, we call for stricter requirements regarding general access to the source code used to produce research results and scientific claims published in the IE literature. Fourth, we describe a set of open source modules for standard IE modeling tasks that represent our first attempt at turning our recommendations into practice. We introduce a Python toolbox for IE that includes the life cycle assessment (LCA) framework Brightway2, the ecospold2matrix module that parses unallocated data in ecospold format, the pySUT and pymrio modules for building and analyzing multiregion input-output models and supply and use tables, and the dynamic stock model class for dynamic stock modeling. Widespread use of open access software can, at the same time, increase quality, transparency, and reproducibility of IE research.

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“…Comprehensive long-term scenarios for metal cycles need to address recycling and therefore need to include dynamic stock models not only for the mineral resources of cobalt, but also for the use phase of cobalt. The combination of dynamic stock models and IO models has been demonstrated already Kagawa et al 2015), and it was applied in an MRIO context by Hertwich et al to estimate the turnover rates of energy installations . The combination of dynamic material stock models with input-output models with a by-product-technology construct, which would allow researchers to study how secondary materials can replace primary production, has to our knowledge not been attempted yet.…”

Section: Recycling and Dynamic Modeling Of The Use Phase Of Cobaltmentioning

confidence: 99%

Matching global cobalt demand under different scenarios for co-production and mining attractiveness

Tisserant

Pauliuk

2016

Economic Structures

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Almost all elements of the periodic table are used in modern technology, especially for renewable energy and communication technologies. Graedel et al. assessed the AbstractMany new and efficient technologies require 'critical metals' to function. These metals are often extracted as by-product of another metal, and their future supply is therefore dependent on mining developments of the host metal. Supply of critical metals can also be constrained because of political instability, discouraging mining policies, or trade restrictions. Scenario analyses of future metal supply that take these factors into account would provide policy makers with information about possible supply shortages. We provide a scenario analysis for demand and supply of cobalt, a potentially critical metal mainly used not only in high performance alloys but also in lithium-ion batteries and catalysts. Cobalt is mainly extracted as by-product of copper and nickel.A multiregional input-output (MRIO) model for 20 world regions and 163 commodities was built from the EXIOBASE v2.2.0 multiregional supply and use table with the commodity technology construct. This MRIO model was hybridized by disaggregating cobalt flows from the nonferrous metal sector. Future cobalt demand in different world regions from 2007 to 2050 was then estimated, assuming region-and sector-specific GDP growth, constant technology, and constant background import shares. A dynamic stock model of regional reserves for seven different types of copper, cobalt, and nickel resources, augmented with optimization-based region-specific mining capacity estimates, was used to determine future cobalt supply. The investment attractiveness index developed by the Fraser Institute specifically for mining industry entered the optimization routine as a measure of the regional attractiveness of mining.The baseline scenario shows no cobalt supply constraints over the considered time period 2007-2050, and recovering about 60 % of cobalt content of the copper and nickel ore flows would be sufficient to match global cobalt demand. When simulating a hypothetical sudden supply dropout in Africa during the period 2020-2035, we found that shortages in cobalt supply might occur in such scenarios.

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Forecasting Replacement Demand of Durable Goods and the Induced Secondary Material Flows: A Case Study of Automobiles

Cited by 18 publications

References 21 publications

A General System Structure and Accounting Framework for Socioeconomic Metabolism

A General System Structure and Accounting Framework for Socioeconomic Metabolism

Lifting Industrial Ecology Modeling to a New Level of Quality and Transparency: A Call for More Transparent Publications and a Collaborative Open Source Software Framework

Matching global cobalt demand under different scenarios for co-production and mining attractiveness

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