Life Cycle Assessment of Seabass (Dicentrarchus labrax) Produced in Offshore Fish Farms: Variability and Multiple Regression Analysis
Equations were developed through multiple regression analysis (MRA) to explain the variability of potential environmental impacts (PEIs) estimated by life cycle assessment (LCA). The case studied refers to the production of seabass in basic offshore fish farms. Contribution analysis showed that the...
Gespeichert in:
Veröffentlicht in: | Sustainability 2019-07, Vol.11 (13), p.3523 |
---|---|
Hauptverfasser: | , , , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | |
---|---|
container_issue | 13 |
container_start_page | 3523 |
container_title | Sustainability |
container_volume | 11 |
creator | García García, Benjamín Rosique Jiménez, Caridad Aguado-Giménez, Felipe García García, José |
description | Equations were developed through multiple regression analysis (MRA) to explain the variability of potential environmental impacts (PEIs) estimated by life cycle assessment (LCA). The case studied refers to the production of seabass in basic offshore fish farms. Contribution analysis showed that the components of the system which most influence the potential environmental impacts are the feed (54% of the overall impact) and the fuel consumed by vessels operating in the farm (23%). Feed and fuel varied widely from one fish farm to another due to different factors, such as the efficiency of the feeding system used in each of them, or the distance from the harbor to the farm. Therefore, a number of scenarios (13) were simulated with different values of both factors and the results of the PEI were fitted by MRA to the model: PEI = a + b × Feed + c × Fuel. For all the PEIs, the regression coefficients were significant (p < 0.05) and R2 was 1. These equations allow us to estimate simply and quickly very different scenarios that reflect the reality of different farms at the present time, but also future scenarios based on the implementation of technologies that will decrease both feed and fuel consumption. |
doi_str_mv | 10.3390/su11133523 |
format | Article |
fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_2533212196</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2533212196</sourcerecordid><originalsourceid>FETCH-LOGICAL-c295t-a8bf6552e55235101ee5bcd095074dd80dd599ddedf23c54dfe831c1360d11c13</originalsourceid><addsrcrecordid>eNpNUF1LAzEQDKJgqb74CwK-qHCaTZq251upVoVKxa_XI5dsbMr1rmbvwPsB_m-vVNCFYZZl2BmGsRMQl0ql4ooaAFBKS7XHelKMIAGhxf6__ZAdE61EN0pBCsMe-54Hj3za2gL5hAiJ1ljWvPL8BU1uiPjZTbDdKZpolw3xwuTRfJ3zp1i5xqLjoeQL72lZReSzQEs-M3FN1_zdxGDyUIS65aZ0_LEp6rDpbJ7xI3Y-oSr5pDRFS4GO2IE3BeHxL_fZ2-z2dXqfzBd3D9PJPLEy1XVixrkfai2xg9IgAFHn1olUi9HAubFwTqepc-i8VFYPnMexAgtqKBxsuc9Od383sfpskOpsVTWxC0GZ1EpJkJAOO9XFTmVjRRTRZ5sY1ia2GYhs23T217T6AeIMcVk</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2533212196</pqid></control><display><type>article</type><title>Life Cycle Assessment of Seabass (Dicentrarchus labrax) Produced in Offshore Fish Farms: Variability and Multiple Regression Analysis</title><source>MDPI - Multidisciplinary Digital Publishing Institute</source><source>EZB-FREE-00999 freely available EZB journals</source><creator>García García, Benjamín ; Rosique Jiménez, Caridad ; Aguado-Giménez, Felipe ; García García, José</creator><creatorcontrib>García García, Benjamín ; Rosique Jiménez, Caridad ; Aguado-Giménez, Felipe ; García García, José</creatorcontrib><description>Equations were developed through multiple regression analysis (MRA) to explain the variability of potential environmental impacts (PEIs) estimated by life cycle assessment (LCA). The case studied refers to the production of seabass in basic offshore fish farms. Contribution analysis showed that the components of the system which most influence the potential environmental impacts are the feed (54% of the overall impact) and the fuel consumed by vessels operating in the farm (23%). Feed and fuel varied widely from one fish farm to another due to different factors, such as the efficiency of the feeding system used in each of them, or the distance from the harbor to the farm. Therefore, a number of scenarios (13) were simulated with different values of both factors and the results of the PEI were fitted by MRA to the model: PEI = a + b × Feed + c × Fuel. For all the PEIs, the regression coefficients were significant (p < 0.05) and R2 was 1. These equations allow us to estimate simply and quickly very different scenarios that reflect the reality of different farms at the present time, but also future scenarios based on the implementation of technologies that will decrease both feed and fuel consumption.</description><identifier>ISSN: 2071-1050</identifier><identifier>EISSN: 2071-1050</identifier><identifier>DOI: 10.3390/su11133523</identifier><language>eng</language><publisher>Basel: MDPI AG</publisher><subject>Aquaculture ; Econometrics ; Eutrophication ; Farms ; Financial analysis ; Fish farms ; Fish hatcheries ; Fuel consumption ; Manufacturing ; Metabolism ; Multiple regression analysis ; Regression analysis ; Regression coefficients ; Sensitivity analysis ; Software</subject><ispartof>Sustainability, 2019-07, Vol.11 (13), p.3523</ispartof><rights>2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c295t-a8bf6552e55235101ee5bcd095074dd80dd599ddedf23c54dfe831c1360d11c13</citedby><cites>FETCH-LOGICAL-c295t-a8bf6552e55235101ee5bcd095074dd80dd599ddedf23c54dfe831c1360d11c13</cites><orcidid>0000-0001-5061-2193</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,777,781,27905,27906</link.rule.ids></links><search><creatorcontrib>García García, Benjamín</creatorcontrib><creatorcontrib>Rosique Jiménez, Caridad</creatorcontrib><creatorcontrib>Aguado-Giménez, Felipe</creatorcontrib><creatorcontrib>García García, José</creatorcontrib><title>Life Cycle Assessment of Seabass (Dicentrarchus labrax) Produced in Offshore Fish Farms: Variability and Multiple Regression Analysis</title><title>Sustainability</title><description>Equations were developed through multiple regression analysis (MRA) to explain the variability of potential environmental impacts (PEIs) estimated by life cycle assessment (LCA). The case studied refers to the production of seabass in basic offshore fish farms. Contribution analysis showed that the components of the system which most influence the potential environmental impacts are the feed (54% of the overall impact) and the fuel consumed by vessels operating in the farm (23%). Feed and fuel varied widely from one fish farm to another due to different factors, such as the efficiency of the feeding system used in each of them, or the distance from the harbor to the farm. Therefore, a number of scenarios (13) were simulated with different values of both factors and the results of the PEI were fitted by MRA to the model: PEI = a + b × Feed + c × Fuel. For all the PEIs, the regression coefficients were significant (p < 0.05) and R2 was 1. These equations allow us to estimate simply and quickly very different scenarios that reflect the reality of different farms at the present time, but also future scenarios based on the implementation of technologies that will decrease both feed and fuel consumption.</description><subject>Aquaculture</subject><subject>Econometrics</subject><subject>Eutrophication</subject><subject>Farms</subject><subject>Financial analysis</subject><subject>Fish farms</subject><subject>Fish hatcheries</subject><subject>Fuel consumption</subject><subject>Manufacturing</subject><subject>Metabolism</subject><subject>Multiple regression analysis</subject><subject>Regression analysis</subject><subject>Regression coefficients</subject><subject>Sensitivity analysis</subject><subject>Software</subject><issn>2071-1050</issn><issn>2071-1050</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><recordid>eNpNUF1LAzEQDKJgqb74CwK-qHCaTZq251upVoVKxa_XI5dsbMr1rmbvwPsB_m-vVNCFYZZl2BmGsRMQl0ql4ooaAFBKS7XHelKMIAGhxf6__ZAdE61EN0pBCsMe-54Hj3za2gL5hAiJ1ljWvPL8BU1uiPjZTbDdKZpolw3xwuTRfJ3zp1i5xqLjoeQL72lZReSzQEs-M3FN1_zdxGDyUIS65aZ0_LEp6rDpbJ7xI3Y-oSr5pDRFS4GO2IE3BeHxL_fZ2-z2dXqfzBd3D9PJPLEy1XVixrkfai2xg9IgAFHn1olUi9HAubFwTqepc-i8VFYPnMexAgtqKBxsuc9Od383sfpskOpsVTWxC0GZ1EpJkJAOO9XFTmVjRRTRZ5sY1ia2GYhs23T217T6AeIMcVk</recordid><startdate>20190701</startdate><enddate>20190701</enddate><creator>García García, Benjamín</creator><creator>Rosique Jiménez, Caridad</creator><creator>Aguado-Giménez, Felipe</creator><creator>García García, José</creator><general>MDPI AG</general><scope>AAYXX</scope><scope>CITATION</scope><scope>4U-</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><orcidid>https://orcid.org/0000-0001-5061-2193</orcidid></search><sort><creationdate>20190701</creationdate><title>Life Cycle Assessment of Seabass (Dicentrarchus labrax) Produced in Offshore Fish Farms: Variability and Multiple Regression Analysis</title><author>García García, Benjamín ; Rosique Jiménez, Caridad ; Aguado-Giménez, Felipe ; García García, José</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c295t-a8bf6552e55235101ee5bcd095074dd80dd599ddedf23c54dfe831c1360d11c13</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Aquaculture</topic><topic>Econometrics</topic><topic>Eutrophication</topic><topic>Farms</topic><topic>Financial analysis</topic><topic>Fish farms</topic><topic>Fish hatcheries</topic><topic>Fuel consumption</topic><topic>Manufacturing</topic><topic>Metabolism</topic><topic>Multiple regression analysis</topic><topic>Regression analysis</topic><topic>Regression coefficients</topic><topic>Sensitivity analysis</topic><topic>Software</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>García García, Benjamín</creatorcontrib><creatorcontrib>Rosique Jiménez, Caridad</creatorcontrib><creatorcontrib>Aguado-Giménez, Felipe</creatorcontrib><creatorcontrib>García García, José</creatorcontrib><collection>CrossRef</collection><collection>University Readers</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Publicly Available Content 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>ProQuest Central China</collection><jtitle>Sustainability</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>García García, Benjamín</au><au>Rosique Jiménez, Caridad</au><au>Aguado-Giménez, Felipe</au><au>García García, José</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Life Cycle Assessment of Seabass (Dicentrarchus labrax) Produced in Offshore Fish Farms: Variability and Multiple Regression Analysis</atitle><jtitle>Sustainability</jtitle><date>2019-07-01</date><risdate>2019</risdate><volume>11</volume><issue>13</issue><spage>3523</spage><pages>3523-</pages><issn>2071-1050</issn><eissn>2071-1050</eissn><abstract>Equations were developed through multiple regression analysis (MRA) to explain the variability of potential environmental impacts (PEIs) estimated by life cycle assessment (LCA). The case studied refers to the production of seabass in basic offshore fish farms. Contribution analysis showed that the components of the system which most influence the potential environmental impacts are the feed (54% of the overall impact) and the fuel consumed by vessels operating in the farm (23%). Feed and fuel varied widely from one fish farm to another due to different factors, such as the efficiency of the feeding system used in each of them, or the distance from the harbor to the farm. Therefore, a number of scenarios (13) were simulated with different values of both factors and the results of the PEI were fitted by MRA to the model: PEI = a + b × Feed + c × Fuel. For all the PEIs, the regression coefficients were significant (p < 0.05) and R2 was 1. These equations allow us to estimate simply and quickly very different scenarios that reflect the reality of different farms at the present time, but also future scenarios based on the implementation of technologies that will decrease both feed and fuel consumption.</abstract><cop>Basel</cop><pub>MDPI AG</pub><doi>10.3390/su11133523</doi><orcidid>https://orcid.org/0000-0001-5061-2193</orcidid><oa>free_for_read</oa></addata></record> |
fulltext | fulltext |
identifier | ISSN: 2071-1050 |
ispartof | Sustainability, 2019-07, Vol.11 (13), p.3523 |
issn | 2071-1050 2071-1050 |
language | eng |
recordid | cdi_proquest_journals_2533212196 |
source | MDPI - Multidisciplinary Digital Publishing Institute; EZB-FREE-00999 freely available EZB journals |
subjects | Aquaculture Econometrics Eutrophication Farms Financial analysis Fish farms Fish hatcheries Fuel consumption Manufacturing Metabolism Multiple regression analysis Regression analysis Regression coefficients Sensitivity analysis Software |
title | Life Cycle Assessment of Seabass (Dicentrarchus labrax) Produced in Offshore Fish Farms: Variability and Multiple Regression Analysis |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-21T08%3A04%3A29IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Life%20Cycle%20Assessment%20of%20Seabass%20(Dicentrarchus%20labrax)%20Produced%20in%20Offshore%20Fish%20Farms:%20Variability%20and%20Multiple%20Regression%20Analysis&rft.jtitle=Sustainability&rft.au=Garc%C3%ADa%20Garc%C3%ADa,%20Benjam%C3%ADn&rft.date=2019-07-01&rft.volume=11&rft.issue=13&rft.spage=3523&rft.pages=3523-&rft.issn=2071-1050&rft.eissn=2071-1050&rft_id=info:doi/10.3390/su11133523&rft_dat=%3Cproquest_cross%3E2533212196%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2533212196&rft_id=info:pmid/&rfr_iscdi=true |