Incremental Material Flow Analysis with Bayesian Inference
Summary Material flow analysis (MFA) is widely used to study the life cycles of materials from production, through use, to reuse, recycling, or disposal, in order to identify environmental impacts and opportunities to address them. However, development of this type of analysis is often constrained b...
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Published in: | Journal of industrial ecology 2018-12, Vol.22 (6), p.1352-1364 |
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container_title | Journal of industrial ecology |
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creator | Lupton, Richard C. Allwood, Julian M. |
description | Summary
Material flow analysis (MFA) is widely used to study the life cycles of materials from production, through use, to reuse, recycling, or disposal, in order to identify environmental impacts and opportunities to address them. However, development of this type of analysis is often constrained by limited data, which may be uncertain, contradictory, missing, or over‐aggregated.
This article proposes a Bayesian approach, in which uncertain knowledge about material flows is described by probability distributions. If little data is initially available, the model predictions will be rather vague. As new data is acquired, it is systematically incorporated to reduce the level of uncertainty.
After reviewing previous approaches to uncertainty in MFA, the Bayesian approach is introduced, and a general recipe for its application to material flow analysis is developed. This is applied to map the global production of steel using Markov Chain Monte Carlo simulations. As well as aiding the analyst, who can get started in the face of incomplete data, this incremental approach to MFA also supports efforts to improve communication of results by transparently accounting for uncertainty throughout. |
doi_str_mv | 10.1111/jiec.12698 |
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Material flow analysis (MFA) is widely used to study the life cycles of materials from production, through use, to reuse, recycling, or disposal, in order to identify environmental impacts and opportunities to address them. However, development of this type of analysis is often constrained by limited data, which may be uncertain, contradictory, missing, or over‐aggregated.
This article proposes a Bayesian approach, in which uncertain knowledge about material flows is described by probability distributions. If little data is initially available, the model predictions will be rather vague. As new data is acquired, it is systematically incorporated to reduce the level of uncertainty.
After reviewing previous approaches to uncertainty in MFA, the Bayesian approach is introduced, and a general recipe for its application to material flow analysis is developed. This is applied to map the global production of steel using Markov Chain Monte Carlo simulations. As well as aiding the analyst, who can get started in the face of incomplete data, this incremental approach to MFA also supports efforts to improve communication of results by transparently accounting for uncertainty throughout.</description><identifier>ISSN: 1088-1980</identifier><identifier>EISSN: 1530-9290</identifier><identifier>DOI: 10.1111/jiec.12698</identifier><language>eng</language><publisher>New Haven: Wiley Subscription Services, Inc</publisher><subject>Bayesian analysis ; Bayesian inference ; Computer simulation ; Data acquisition ; Data processing ; Environmental impact ; industrial ecology ; Iron and steel making ; Life cycles ; Markov analysis ; Markov Chain Monte Carlo ; Markov chains ; material flow analysis ; Monte Carlo simulation ; Recycling ; Reuse ; Statistical inference ; Steel ; Uncertainty</subject><ispartof>Journal of industrial ecology, 2018-12, Vol.22 (6), p.1352-1364</ispartof><rights>2017 The Authors. , published by Wiley Periodicals, Inc., on behalf of Yale University.</rights><rights>Copyright © 2018, Yale University</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c3378-9c1089215fa73c81cfaea05d3f287cb97ccb136b7842662d2fad2963749493523</citedby><cites>FETCH-LOGICAL-c3378-9c1089215fa73c81cfaea05d3f287cb97ccb136b7842662d2fad2963749493523</cites><orcidid>0000-0001-8622-3085</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,27901,27902,33200</link.rule.ids></links><search><creatorcontrib>Lupton, Richard C.</creatorcontrib><creatorcontrib>Allwood, Julian M.</creatorcontrib><title>Incremental Material Flow Analysis with Bayesian Inference</title><title>Journal of industrial ecology</title><description>Summary
Material flow analysis (MFA) is widely used to study the life cycles of materials from production, through use, to reuse, recycling, or disposal, in order to identify environmental impacts and opportunities to address them. However, development of this type of analysis is often constrained by limited data, which may be uncertain, contradictory, missing, or over‐aggregated.
This article proposes a Bayesian approach, in which uncertain knowledge about material flows is described by probability distributions. If little data is initially available, the model predictions will be rather vague. As new data is acquired, it is systematically incorporated to reduce the level of uncertainty.
After reviewing previous approaches to uncertainty in MFA, the Bayesian approach is introduced, and a general recipe for its application to material flow analysis is developed. This is applied to map the global production of steel using Markov Chain Monte Carlo simulations. As well as aiding the analyst, who can get started in the face of incomplete data, this incremental approach to MFA also supports efforts to improve communication of results by transparently accounting for uncertainty throughout.</description><subject>Bayesian analysis</subject><subject>Bayesian inference</subject><subject>Computer simulation</subject><subject>Data acquisition</subject><subject>Data processing</subject><subject>Environmental impact</subject><subject>industrial ecology</subject><subject>Iron and steel making</subject><subject>Life cycles</subject><subject>Markov analysis</subject><subject>Markov Chain Monte Carlo</subject><subject>Markov chains</subject><subject>material flow analysis</subject><subject>Monte Carlo simulation</subject><subject>Recycling</subject><subject>Reuse</subject><subject>Statistical inference</subject><subject>Steel</subject><subject>Uncertainty</subject><issn>1088-1980</issn><issn>1530-9290</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><sourceid>24P</sourceid><sourceid>8BJ</sourceid><recordid>eNp9kM1OwzAQhC0EEqVw4QkicUNK8U8S29xK1UJQERc4W46zFo7SpNipqrw9LuHMXnYO386OBqFbghckzkPjwCwILaQ4QzOSM5xKKvF51FiIlEiBL9FVCA3GhBUUz9Bj2RkPO-gG3SZvegDvoti0_TFZdrodgwvJ0Q1fyZMeITjdJWVnwUNn4BpdWN0GuPnbc_S5WX-sXtLt-3O5Wm5TwxgXqTTxt6Qkt5ozI4ixGjTOa2ap4KaS3Jgqhqm4yGhR0JpaXVNZMJ7JTLKcsjm6m3z3vv8-QBhU0x98DBcUJdFXcppnkbqfKOP7EDxYtfdup_2oCFanbtSpG_XbTYTJBB9dC-M_pHot16vp5gfPgGTa</recordid><startdate>201812</startdate><enddate>201812</enddate><creator>Lupton, Richard C.</creator><creator>Allwood, Julian M.</creator><general>Wiley Subscription Services, Inc</general><scope>24P</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7ST</scope><scope>8BJ</scope><scope>C1K</scope><scope>FQK</scope><scope>JBE</scope><scope>SOI</scope><orcidid>https://orcid.org/0000-0001-8622-3085</orcidid></search><sort><creationdate>201812</creationdate><title>Incremental Material Flow Analysis with Bayesian Inference</title><author>Lupton, Richard C. ; Allwood, Julian M.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3378-9c1089215fa73c81cfaea05d3f287cb97ccb136b7842662d2fad2963749493523</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Bayesian analysis</topic><topic>Bayesian inference</topic><topic>Computer simulation</topic><topic>Data acquisition</topic><topic>Data processing</topic><topic>Environmental impact</topic><topic>industrial ecology</topic><topic>Iron and steel making</topic><topic>Life cycles</topic><topic>Markov analysis</topic><topic>Markov Chain Monte Carlo</topic><topic>Markov chains</topic><topic>material flow analysis</topic><topic>Monte Carlo simulation</topic><topic>Recycling</topic><topic>Reuse</topic><topic>Statistical inference</topic><topic>Steel</topic><topic>Uncertainty</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Lupton, Richard C.</creatorcontrib><creatorcontrib>Allwood, Julian M.</creatorcontrib><collection>Wiley Online Library Open Access</collection><collection>CrossRef</collection><collection>Environment Abstracts</collection><collection>International Bibliography of the Social Sciences (IBSS)</collection><collection>Environmental Sciences and Pollution Management</collection><collection>International Bibliography of the Social Sciences</collection><collection>International Bibliography of the Social Sciences</collection><collection>Environment Abstracts</collection><jtitle>Journal of industrial ecology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Lupton, Richard C.</au><au>Allwood, Julian M.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Incremental Material Flow Analysis with Bayesian Inference</atitle><jtitle>Journal of industrial ecology</jtitle><date>2018-12</date><risdate>2018</risdate><volume>22</volume><issue>6</issue><spage>1352</spage><epage>1364</epage><pages>1352-1364</pages><issn>1088-1980</issn><eissn>1530-9290</eissn><abstract>Summary
Material flow analysis (MFA) is widely used to study the life cycles of materials from production, through use, to reuse, recycling, or disposal, in order to identify environmental impacts and opportunities to address them. However, development of this type of analysis is often constrained by limited data, which may be uncertain, contradictory, missing, or over‐aggregated.
This article proposes a Bayesian approach, in which uncertain knowledge about material flows is described by probability distributions. If little data is initially available, the model predictions will be rather vague. As new data is acquired, it is systematically incorporated to reduce the level of uncertainty.
After reviewing previous approaches to uncertainty in MFA, the Bayesian approach is introduced, and a general recipe for its application to material flow analysis is developed. This is applied to map the global production of steel using Markov Chain Monte Carlo simulations. As well as aiding the analyst, who can get started in the face of incomplete data, this incremental approach to MFA also supports efforts to improve communication of results by transparently accounting for uncertainty throughout.</abstract><cop>New Haven</cop><pub>Wiley Subscription Services, Inc</pub><doi>10.1111/jiec.12698</doi><tpages>13</tpages><orcidid>https://orcid.org/0000-0001-8622-3085</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Bayesian analysis Bayesian inference Computer simulation Data acquisition Data processing Environmental impact industrial ecology Iron and steel making Life cycles Markov analysis Markov Chain Monte Carlo Markov chains material flow analysis Monte Carlo simulation Recycling Reuse Statistical inference Steel Uncertainty |
title | Incremental Material Flow Analysis with Bayesian Inference |
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