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Development of weighting factors for G20 countries. Part 2: estimation of willingness to pay and annual global damage cost
Purpose This paper is the second part of a series of articles presenting the results of research on monetary weighting factors (MWFs) for the G20 countries, which together account for approximately 90% of the global GDP. We developed their MWFs with regard to Life Cycle Impact Assessment (LCIA) and...
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Published in: | The international journal of life cycle assessment 2018-12, Vol.23 (12), p.2349-2364 |
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creator | Murakami, Kayo Itsubo, Norihiro Kuriyama, Koichi Yoshida, Kentaro Tokimatsu, Koji |
description | Purpose
This paper is the second part of a series of articles presenting the results of research on monetary weighting factors (MWFs) for the G20 countries, which together account for approximately 90% of the global GDP. We developed their MWFs with regard to Life Cycle Impact Assessment (LCIA) and evaluated them via a large-scale questionnaire survey. We estimated the economic value of one unit of damage caused by human activities.
Methods
To ensure that the MWFs covered all areas of protection as defined by the LCIA method based on Endpoint Modeling (human health, social assets, biodiversity, and primary production), we conducted a choice experiment in all G20 countries. We conducted face-to-face interviews to minimize survey bias and ensure that the questions were understood by the emerging G20 countries’ respondents. Internet surveys were adopted to collect samples from the developed G20 countries’ respondents, where Internet diffusion rates are generally high. We obtained response data from 200 to 250 and 500 to 600 households of all the emerging and all the developed G20 countries, respectively. We gathered 6400 responses in all. We estimated preference intensities using the random parameter logit model. We calculated MWFs based on each respondent’s willingness to pay.
Results and discussion
We devised MWFs providing the costs of damage to four safeguard subjects. All the estimated values are statistically significant at the 1% level, with the exception of monetary attributes from Mexico. The MWFs for the G20 are 23,000 USD for human health (per year), 2.5 USD for social assets (per USD of resources), 11 billion USD for biodiversity (per species), and 5.6 billion USD for primary production (per 100 million tons). The differences between the developed and emerging G20 countries are considerable, with the values generally being smaller for the latter in purchasing power parity (USD) terms. The estimated global total economic annual impact was approximately 5.1 trillion USD (6.7% of the world’s total GDP).
Conclusions
We obtained reasonable and conservative global-scale MWFs compared with previous studies. Moreover, the cross-country heterogeneity in this study potentially helps extrapolate future/global value developments from current/local estimates. The variations in human health and social asset MWFs are small enough within developed countries to allow international transfers among them, while significant variations in biodiversity and primary produ |
doi_str_mv | 10.1007/s11367-017-1372-1 |
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This paper is the second part of a series of articles presenting the results of research on monetary weighting factors (MWFs) for the G20 countries, which together account for approximately 90% of the global GDP. We developed their MWFs with regard to Life Cycle Impact Assessment (LCIA) and evaluated them via a large-scale questionnaire survey. We estimated the economic value of one unit of damage caused by human activities.
Methods
To ensure that the MWFs covered all areas of protection as defined by the LCIA method based on Endpoint Modeling (human health, social assets, biodiversity, and primary production), we conducted a choice experiment in all G20 countries. We conducted face-to-face interviews to minimize survey bias and ensure that the questions were understood by the emerging G20 countries’ respondents. Internet surveys were adopted to collect samples from the developed G20 countries’ respondents, where Internet diffusion rates are generally high. We obtained response data from 200 to 250 and 500 to 600 households of all the emerging and all the developed G20 countries, respectively. We gathered 6400 responses in all. We estimated preference intensities using the random parameter logit model. We calculated MWFs based on each respondent’s willingness to pay.
Results and discussion
We devised MWFs providing the costs of damage to four safeguard subjects. All the estimated values are statistically significant at the 1% level, with the exception of monetary attributes from Mexico. The MWFs for the G20 are 23,000 USD for human health (per year), 2.5 USD for social assets (per USD of resources), 11 billion USD for biodiversity (per species), and 5.6 billion USD for primary production (per 100 million tons). The differences between the developed and emerging G20 countries are considerable, with the values generally being smaller for the latter in purchasing power parity (USD) terms. The estimated global total economic annual impact was approximately 5.1 trillion USD (6.7% of the world’s total GDP).
Conclusions
We obtained reasonable and conservative global-scale MWFs compared with previous studies. Moreover, the cross-country heterogeneity in this study potentially helps extrapolate future/global value developments from current/local estimates. The variations in human health and social asset MWFs are small enough within developed countries to allow international transfers among them, while significant variations in biodiversity and primary production MWFs are a caveat to up-front international transfers even within developed countries.</description><identifier>ISSN: 0948-3349</identifier><identifier>EISSN: 1614-7502</identifier><identifier>DOI: 10.1007/s11367-017-1372-1</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Biodiversity ; Damage ; Developed countries ; Development of Global Scale Lcia Method ; Diffusion rate ; Earth and Environmental Science ; Economics ; Environment ; Environmental Chemistry ; Environmental Economics ; Environmental Engineering/Biotechnology ; GDP ; Gross Domestic Product ; Health ; Heterogeneity ; Households ; Human influences ; Impact analysis ; Industrialized nations ; Internet ; Life cycle assessment ; Life cycle engineering ; Life cycles ; Logit models ; Polls & surveys ; Primary production ; Statistical analysis ; Statistical methods ; Weighting ; Willingness to pay</subject><ispartof>The international journal of life cycle assessment, 2018-12, Vol.23 (12), p.2349-2364</ispartof><rights>Springer-Verlag GmbH Germany 2017</rights><rights>Springer-Verlag GmbH Germany 2017.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c430t-2b768427acd497f5b156edbf711932e0ccf41509561adcb63351cdb3ebccffaf3</citedby><cites>FETCH-LOGICAL-c430t-2b768427acd497f5b156edbf711932e0ccf41509561adcb63351cdb3ebccffaf3</cites><orcidid>0000-0002-6479-9240</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids></links><search><creatorcontrib>Murakami, Kayo</creatorcontrib><creatorcontrib>Itsubo, Norihiro</creatorcontrib><creatorcontrib>Kuriyama, Koichi</creatorcontrib><creatorcontrib>Yoshida, Kentaro</creatorcontrib><creatorcontrib>Tokimatsu, Koji</creatorcontrib><title>Development of weighting factors for G20 countries. Part 2: estimation of willingness to pay and annual global damage cost</title><title>The international journal of life cycle assessment</title><addtitle>Int J Life Cycle Assess</addtitle><description>Purpose
This paper is the second part of a series of articles presenting the results of research on monetary weighting factors (MWFs) for the G20 countries, which together account for approximately 90% of the global GDP. We developed their MWFs with regard to Life Cycle Impact Assessment (LCIA) and evaluated them via a large-scale questionnaire survey. We estimated the economic value of one unit of damage caused by human activities.
Methods
To ensure that the MWFs covered all areas of protection as defined by the LCIA method based on Endpoint Modeling (human health, social assets, biodiversity, and primary production), we conducted a choice experiment in all G20 countries. We conducted face-to-face interviews to minimize survey bias and ensure that the questions were understood by the emerging G20 countries’ respondents. Internet surveys were adopted to collect samples from the developed G20 countries’ respondents, where Internet diffusion rates are generally high. We obtained response data from 200 to 250 and 500 to 600 households of all the emerging and all the developed G20 countries, respectively. We gathered 6400 responses in all. We estimated preference intensities using the random parameter logit model. We calculated MWFs based on each respondent’s willingness to pay.
Results and discussion
We devised MWFs providing the costs of damage to four safeguard subjects. All the estimated values are statistically significant at the 1% level, with the exception of monetary attributes from Mexico. The MWFs for the G20 are 23,000 USD for human health (per year), 2.5 USD for social assets (per USD of resources), 11 billion USD for biodiversity (per species), and 5.6 billion USD for primary production (per 100 million tons). The differences between the developed and emerging G20 countries are considerable, with the values generally being smaller for the latter in purchasing power parity (USD) terms. The estimated global total economic annual impact was approximately 5.1 trillion USD (6.7% of the world’s total GDP).
Conclusions
We obtained reasonable and conservative global-scale MWFs compared with previous studies. Moreover, the cross-country heterogeneity in this study potentially helps extrapolate future/global value developments from current/local estimates. The variations in human health and social asset MWFs are small enough within developed countries to allow international transfers among them, while significant variations in biodiversity and primary production MWFs are a caveat to up-front international transfers even within developed countries.</description><subject>Biodiversity</subject><subject>Damage</subject><subject>Developed countries</subject><subject>Development of Global Scale Lcia Method</subject><subject>Diffusion rate</subject><subject>Earth and Environmental Science</subject><subject>Economics</subject><subject>Environment</subject><subject>Environmental Chemistry</subject><subject>Environmental Economics</subject><subject>Environmental Engineering/Biotechnology</subject><subject>GDP</subject><subject>Gross Domestic Product</subject><subject>Health</subject><subject>Heterogeneity</subject><subject>Households</subject><subject>Human influences</subject><subject>Impact analysis</subject><subject>Industrialized nations</subject><subject>Internet</subject><subject>Life cycle assessment</subject><subject>Life cycle engineering</subject><subject>Life cycles</subject><subject>Logit models</subject><subject>Polls & surveys</subject><subject>Primary production</subject><subject>Statistical analysis</subject><subject>Statistical methods</subject><subject>Weighting</subject><subject>Willingness to pay</subject><issn>0948-3349</issn><issn>1614-7502</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><recordid>eNp1kEFPxCAQhYnRxHX1B3gj8dyVgba03syqq4mJHvRMKIXaTRdWoJr118taE08eZubAe2-YD6FzIAsghF8GAFbyjADPgHGawQGaQQl5xgtCD9GM1HmVMZbXx-gkhDUhFEhdzNDXjf7Qg9tutI3YGfyp--4t9rbDRqrofMDGebyiBCs32uh7HRb4WfqI6RXWIfYbGXtnf6z9MCSj1SHg6PBW7rC0bSo7ygF3g2vSaOVGdjqFhXiKjowcgj77nXP0enf7srzPHp9WD8vrx0zljMSMNryscsqlavOam6KBotRtYzhAzagmSpkcinRMCbJVTclYAaptmG7Si5GGzdHFlLv17n1MfxZrN3qbVgqaoFU5q4oqqWBSKe9C8NqIrU_H-Z0AIvaIxYRYJMRijzi1OaKTJySt7bT_S_7f9A2B5n_U</recordid><startdate>20181201</startdate><enddate>20181201</enddate><creator>Murakami, Kayo</creator><creator>Itsubo, Norihiro</creator><creator>Kuriyama, Koichi</creator><creator>Yoshida, Kentaro</creator><creator>Tokimatsu, Koji</creator><general>Springer Berlin Heidelberg</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7ST</scope><scope>7TB</scope><scope>7XB</scope><scope>88I</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FK</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>C1K</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>F28</scope><scope>FR3</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>KR7</scope><scope>L6V</scope><scope>M2P</scope><scope>M7S</scope><scope>PATMY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PTHSS</scope><scope>PYCSY</scope><scope>Q9U</scope><scope>SOI</scope><orcidid>https://orcid.org/0000-0002-6479-9240</orcidid></search><sort><creationdate>20181201</creationdate><title>Development of weighting factors for G20 countries. Part 2: estimation of willingness to pay and annual global damage cost</title><author>Murakami, Kayo ; Itsubo, Norihiro ; Kuriyama, Koichi ; Yoshida, Kentaro ; Tokimatsu, Koji</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c430t-2b768427acd497f5b156edbf711932e0ccf41509561adcb63351cdb3ebccffaf3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Biodiversity</topic><topic>Damage</topic><topic>Developed countries</topic><topic>Development of Global Scale Lcia Method</topic><topic>Diffusion rate</topic><topic>Earth and Environmental Science</topic><topic>Economics</topic><topic>Environment</topic><topic>Environmental Chemistry</topic><topic>Environmental Economics</topic><topic>Environmental Engineering/Biotechnology</topic><topic>GDP</topic><topic>Gross Domestic Product</topic><topic>Health</topic><topic>Heterogeneity</topic><topic>Households</topic><topic>Human influences</topic><topic>Impact analysis</topic><topic>Industrialized nations</topic><topic>Internet</topic><topic>Life cycle assessment</topic><topic>Life cycle engineering</topic><topic>Life cycles</topic><topic>Logit models</topic><topic>Polls & surveys</topic><topic>Primary production</topic><topic>Statistical analysis</topic><topic>Statistical methods</topic><topic>Weighting</topic><topic>Willingness to pay</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Murakami, Kayo</creatorcontrib><creatorcontrib>Itsubo, Norihiro</creatorcontrib><creatorcontrib>Kuriyama, Koichi</creatorcontrib><creatorcontrib>Yoshida, Kentaro</creatorcontrib><creatorcontrib>Tokimatsu, Koji</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Environment Abstracts</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Science Database (Alumni Edition)</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>Civil Engineering Abstracts</collection><collection>ProQuest Engineering Collection</collection><collection>Science Database</collection><collection>Engineering Database</collection><collection>Environmental Science Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>Engineering collection</collection><collection>Environmental Science Collection</collection><collection>ProQuest Central Basic</collection><collection>Environment Abstracts</collection><jtitle>The international journal of life cycle assessment</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Murakami, Kayo</au><au>Itsubo, Norihiro</au><au>Kuriyama, Koichi</au><au>Yoshida, Kentaro</au><au>Tokimatsu, Koji</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Development of weighting factors for G20 countries. Part 2: estimation of willingness to pay and annual global damage cost</atitle><jtitle>The international journal of life cycle assessment</jtitle><stitle>Int J Life Cycle Assess</stitle><date>2018-12-01</date><risdate>2018</risdate><volume>23</volume><issue>12</issue><spage>2349</spage><epage>2364</epage><pages>2349-2364</pages><issn>0948-3349</issn><eissn>1614-7502</eissn><abstract>Purpose
This paper is the second part of a series of articles presenting the results of research on monetary weighting factors (MWFs) for the G20 countries, which together account for approximately 90% of the global GDP. We developed their MWFs with regard to Life Cycle Impact Assessment (LCIA) and evaluated them via a large-scale questionnaire survey. We estimated the economic value of one unit of damage caused by human activities.
Methods
To ensure that the MWFs covered all areas of protection as defined by the LCIA method based on Endpoint Modeling (human health, social assets, biodiversity, and primary production), we conducted a choice experiment in all G20 countries. We conducted face-to-face interviews to minimize survey bias and ensure that the questions were understood by the emerging G20 countries’ respondents. Internet surveys were adopted to collect samples from the developed G20 countries’ respondents, where Internet diffusion rates are generally high. We obtained response data from 200 to 250 and 500 to 600 households of all the emerging and all the developed G20 countries, respectively. We gathered 6400 responses in all. We estimated preference intensities using the random parameter logit model. We calculated MWFs based on each respondent’s willingness to pay.
Results and discussion
We devised MWFs providing the costs of damage to four safeguard subjects. All the estimated values are statistically significant at the 1% level, with the exception of monetary attributes from Mexico. The MWFs for the G20 are 23,000 USD for human health (per year), 2.5 USD for social assets (per USD of resources), 11 billion USD for biodiversity (per species), and 5.6 billion USD for primary production (per 100 million tons). The differences between the developed and emerging G20 countries are considerable, with the values generally being smaller for the latter in purchasing power parity (USD) terms. The estimated global total economic annual impact was approximately 5.1 trillion USD (6.7% of the world’s total GDP).
Conclusions
We obtained reasonable and conservative global-scale MWFs compared with previous studies. Moreover, the cross-country heterogeneity in this study potentially helps extrapolate future/global value developments from current/local estimates. The variations in human health and social asset MWFs are small enough within developed countries to allow international transfers among them, while significant variations in biodiversity and primary production MWFs are a caveat to up-front international transfers even within developed countries.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><doi>10.1007/s11367-017-1372-1</doi><tpages>16</tpages><orcidid>https://orcid.org/0000-0002-6479-9240</orcidid></addata></record> |
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subjects | Biodiversity Damage Developed countries Development of Global Scale Lcia Method Diffusion rate Earth and Environmental Science Economics Environment Environmental Chemistry Environmental Economics Environmental Engineering/Biotechnology GDP Gross Domestic Product Health Heterogeneity Households Human influences Impact analysis Industrialized nations Internet Life cycle assessment Life cycle engineering Life cycles Logit models Polls & surveys Primary production Statistical analysis Statistical methods Weighting Willingness to pay |
title | Development of weighting factors for G20 countries. Part 2: estimation of willingness to pay and annual global damage cost |
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