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...

Full description

Saved in:
Bibliographic Details
Published in:Sustainability 2019-07, Vol.11 (13), p.3523
Main Authors: García García, Benjamín, Rosique Jiménez, Caridad, Aguado-Giménez, Felipe, García García, José
Format: Article
Language:English
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
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:2071-1050
2071-1050
DOI:10.3390/su11133523