A material footprint model for green information systems – using statistical learning to identify the predictors of natural resource use

Green Information Systems in general, and footprint calculators in particular, are promising feedback tools to assist people in adopting sustainable behaviour. Therefore, a Material Footprint model for use in an online footprint calculator was developed by identifying the most important predictors o...

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Autores principales:	Johannes Buhl, Christa Liedtke, Jens Teubler, Sebastian Schuster, Katrin Bienge
Formato:	article
Lenguaje:	EN
Publicado:	Taylor & Francis Group 2019
Materias:	sustainable consumption machine learning data mining online survey feedback Engineering (General). Civil engineering (General) TA1-2040
Acceso en línea:	https://doaj.org/article/ced993f4d4c0490f96569eacad55731e
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spelling	oai:doaj.org-article:ced993f4d4c0490f96569eacad55731e2021-11-04T15:51:55ZA material footprint model for green information systems – using statistical learning to identify the predictors of natural resource use2331-191610.1080/23311916.2019.1616655https://doaj.org/article/ced993f4d4c0490f96569eacad55731e2019-01-01T00:00:00Zhttp://dx.doi.org/10.1080/23311916.2019.1616655https://doaj.org/toc/2331-1916Green Information Systems in general, and footprint calculators in particular, are promising feedback tools to assist people in adopting sustainable behaviour. Therefore, a Material Footprint model for use in an online footprint calculator was developed by identifying the most important predictors of the Material Footprint of the calculator’s users. By means of statistical learning, the analysis revealed that 22 of the 95 predictors identified accounted for 74% of the variance in Material Footprints. Ten predictors out of the 95, mainly from the mobility domain, were capable of showing a prediction accuracy of 61%. The authors conclude that 22 predictors from the areas of mobility, housing and nutrition, as well as sociodemographic information, accurately predict a person’s Material Footprint. The short and concise Material Footprint model may help developers and researchers to enhance their information systems with additional items while ensuring the data quality of such applications.Johannes BuhlChrista LiedtkeJens TeublerSebastian SchusterKatrin BiengeTaylor & Francis Grouparticlesustainable consumptionmachine learningdata miningonline surveyfeedbackEngineering (General). Civil engineering (General)TA1-2040ENCogent Engineering, Vol 6, Iss 1 (2019)
institution	DOAJ
collection	DOAJ
language	EN
topic	sustainable consumption machine learning data mining online survey feedback Engineering (General). Civil engineering (General) TA1-2040
spellingShingle	sustainable consumption machine learning data mining online survey feedback Engineering (General). Civil engineering (General) TA1-2040 Johannes Buhl Christa Liedtke Jens Teubler Sebastian Schuster Katrin Bienge A material footprint model for green information systems – using statistical learning to identify the predictors of natural resource use
description	Green Information Systems in general, and footprint calculators in particular, are promising feedback tools to assist people in adopting sustainable behaviour. Therefore, a Material Footprint model for use in an online footprint calculator was developed by identifying the most important predictors of the Material Footprint of the calculator’s users. By means of statistical learning, the analysis revealed that 22 of the 95 predictors identified accounted for 74% of the variance in Material Footprints. Ten predictors out of the 95, mainly from the mobility domain, were capable of showing a prediction accuracy of 61%. The authors conclude that 22 predictors from the areas of mobility, housing and nutrition, as well as sociodemographic information, accurately predict a person’s Material Footprint. The short and concise Material Footprint model may help developers and researchers to enhance their information systems with additional items while ensuring the data quality of such applications.
format	article
author	Johannes Buhl Christa Liedtke Jens Teubler Sebastian Schuster Katrin Bienge
author_facet	Johannes Buhl Christa Liedtke Jens Teubler Sebastian Schuster Katrin Bienge
author_sort	Johannes Buhl
title	A material footprint model for green information systems – using statistical learning to identify the predictors of natural resource use
title_short	A material footprint model for green information systems – using statistical learning to identify the predictors of natural resource use
title_full	A material footprint model for green information systems – using statistical learning to identify the predictors of natural resource use
title_fullStr	A material footprint model for green information systems – using statistical learning to identify the predictors of natural resource use
title_full_unstemmed	A material footprint model for green information systems – using statistical learning to identify the predictors of natural resource use
title_sort	material footprint model for green information systems – using statistical learning to identify the predictors of natural resource use
publisher	Taylor & Francis Group
publishDate	2019
url	https://doaj.org/article/ced993f4d4c0490f96569eacad55731e
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A material footprint model for green information systems – using statistical learning to identify the predictors of natural resource use

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