Machine learning for spatial disaggregation of regional transport data in the EU

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The European Union (EU) is actively working to combat climate change and promote sustainable development by reducing greenhouse gas (GHG) emissions. The transport sector, a major contributor of GHG emissions, was at the forefront of these initiatives. After experiencing steady growth from 2013 until...

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Autor principal: Fernandez, Juan R.
Otros Autores: Ates, Cihan
Formato: Tesis de maestría
Lenguaje:Inglés
Publicado: 2024
Materias:
UNIÓN EUROPEA
TRANSPORTE
DESCARBONIZACIÓN
EUROPA
EMISIÓN DE CARBONO
APRENDIZAJE AUTOMÁTICO
DESAGREGACIÓN ESPACIAL
Acceso en línea:https://ri.itba.edu.ar/handle/20.500.14769/4324
Aporte de:
Repositorio Institucional Instituto Tecnológico de Buenos Aires (ITBA) de Instituto Tecnológico de Buenos Aires (ITBA)
id I32-R138-20.500.14769-4324
record_format dspace
spelling I32-R138-20.500.14769-43242026-01-15T14:38:04Z Machine learning for spatial disaggregation of regional transport data in the EU Fernandez, Juan R. Ates, Cihan Patil, Shruthi UNIÓN EUROPEA TRANSPORTE DESCARBONIZACIÓN EUROPA EMISIÓN DE CARBONO APRENDIZAJE AUTOMÁTICO DESAGREGACIÓN ESPACIAL The European Union (EU) is actively working to combat climate change and promote sustainable development by reducing greenhouse gas (GHG) emissions. The transport sector, a major contributor of GHG emissions, was at the forefront of these initiatives. After experiencing steady growth from 2013 until 2019, there was an abrupt decrease in 2020 due to the COVID-19 pandemic. However, preliminary estimates indicated a rebound of 7.7% for transport emissions in 2021, according to the [Agency (2021)]. Nonetheless, further research is necessary in order to devise effective strategies for regional decarbonization within this challenging sector. An analysis of the transport sector in Europe reveals significant disparities in emission trends across different regions. According to [Eurostat (2021)], Western European countries have generally experienced greater decreases in transport emissions compared to Central and Eastern European nations, which have made slower progress. Furthermore, the European Environment Agency [Agency (2021)] points out that urban areas tend to have higher emissions due to higher population densities and greater demand for transportation. These discrepancies underscore the necessity for spatial disaggregation when developing tailored decarbonization strategies for different regions. To address the intricacies of regional decarbonization potentials, this research aims to apply machine learning techniques to enhance the accuracy of estimating transport-related metrics at a regional level. This, in turn, will facilitate the identification of decarbonization opportunities within the transport sector. More precisely, the study seeks to establish a framework that utilizes machine learning methodologies for spatial disaggregation, a critical process for understanding the factors that influence emissions on a regional scale and devising efficient mitigation strategies. 2024-03-05T14:15:03Z 2024-03-05T14:15:03Z 2023-06-23 Tesis de maestría https://ri.itba.edu.ar/handle/20.500.14769/4324 en application/pdf
institution Instituto Tecnológico de Buenos Aires (ITBA)
institution_str I-32
repository_str R-138
collection Repositorio Institucional Instituto Tecnológico de Buenos Aires (ITBA)
language Inglés
topic UNIÓN EUROPEA
TRANSPORTE
DESCARBONIZACIÓN
EUROPA
EMISIÓN DE CARBONO
APRENDIZAJE AUTOMÁTICO
DESAGREGACIÓN ESPACIAL
spellingShingle UNIÓN EUROPEA
TRANSPORTE
DESCARBONIZACIÓN
EUROPA
EMISIÓN DE CARBONO
APRENDIZAJE AUTOMÁTICO
DESAGREGACIÓN ESPACIAL
Fernandez, Juan R.
Machine learning for spatial disaggregation of regional transport data in the EU
topic_facet UNIÓN EUROPEA
TRANSPORTE
DESCARBONIZACIÓN
EUROPA
EMISIÓN DE CARBONO
APRENDIZAJE AUTOMÁTICO
DESAGREGACIÓN ESPACIAL
description The European Union (EU) is actively working to combat climate change and promote sustainable development by reducing greenhouse gas (GHG) emissions. The transport sector, a major contributor of GHG emissions, was at the forefront of these initiatives. After experiencing steady growth from 2013 until 2019, there was an abrupt decrease in 2020 due to the COVID-19 pandemic. However, preliminary estimates indicated a rebound of 7.7% for transport emissions in 2021, according to the [Agency (2021)]. Nonetheless, further research is necessary in order to devise effective strategies for regional decarbonization within this challenging sector. An analysis of the transport sector in Europe reveals significant disparities in emission trends across different regions. According to [Eurostat (2021)], Western European countries have generally experienced greater decreases in transport emissions compared to Central and Eastern European nations, which have made slower progress. Furthermore, the European Environment Agency [Agency (2021)] points out that urban areas tend to have higher emissions due to higher population densities and greater demand for transportation. These discrepancies underscore the necessity for spatial disaggregation when developing tailored decarbonization strategies for different regions. To address the intricacies of regional decarbonization potentials, this research aims to apply machine learning techniques to enhance the accuracy of estimating transport-related metrics at a regional level. This, in turn, will facilitate the identification of decarbonization opportunities within the transport sector. More precisely, the study seeks to establish a framework that utilizes machine learning methodologies for spatial disaggregation, a critical process for understanding the factors that influence emissions on a regional scale and devising efficient mitigation strategies.
author2 Ates, Cihan
author_facet Ates, Cihan
Fernandez, Juan R.
format Tesis de maestría
author Fernandez, Juan R.
author_sort Fernandez, Juan R.
title Machine learning for spatial disaggregation of regional transport data in the EU
title_short Machine learning for spatial disaggregation of regional transport data in the EU
title_full Machine learning for spatial disaggregation of regional transport data in the EU
title_fullStr Machine learning for spatial disaggregation of regional transport data in the EU
title_full_unstemmed Machine learning for spatial disaggregation of regional transport data in the EU
title_sort machine learning for spatial disaggregation of regional transport data in the eu
publishDate 2024
url https://ri.itba.edu.ar/handle/20.500.14769/4324
work_keys_str_mv AT fernandezjuanr machinelearningforspatialdisaggregationofregionaltransportdataintheeu
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