Evaluating the effects of uncertainty on projections of greenhouse gas emissions : a biofuel case study in Brazil

Detalhes bibliográficos
Autor(a) principal: Barroso, Renan Maron
Data de Publicação: 2019
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Texto Completo: http://hdl.handle.net/10362/67515
Resumo: Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial Technologies
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spelling Evaluating the effects of uncertainty on projections of greenhouse gas emissions : a biofuel case study in BrazilGreenhouse gas emissionsLand use changesLand use change projectionsMitigation measuresBrazilCarbon stocksBiofuelUncertaintyStochastic modellingMonte Carlo simulationDissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial TechnologiesThe use of projections of greenhouse gas emissions (GHG) estimates are fundamental to design appropriate policies to combat climate change, but the inherent complex nature of the climate system results in projections with a significant degree of uncertainty. An important source of uncertainty in GHG emissions estimates refers to land use changes (LUC) due to the complexity of the land system. As the land domain plays a relevant role in climate change mitigation, understanding the effects of uncertainty on projections of LUC-related GHG emissions estimates is crucial to better support the process of decision making. Based on a case study conducted by van der Hilst et al. (2018), this thesis evaluates the effects of uncertainty on the projections of LUC-related GHG emissions in Brazil towards 2030, given an expected increase in the global biofuel demand and distinct scenarios of LUC mitigation measures. With the use of Monte Carlo simulation technique, we developed a spatially explicit, stochastic model in Python programming language to perform the uncertainty analysis. As uncertainty can be derived from many sources, we focused on adding uncertainty in the model input data to assess its effects on the LUC-related GHG emissions estimates resulting from an increase in the global biofuel demand. As van der Hilst et al. (2018) performed an analysis of the same case study, but without uncertainty analysis, this thesis compares the stochastic results of the deterministic results. The comparison of the results obtained between the deterministic and the stochastic approach provides valuable insights about the effects of uncertainty in the final estimates of emissions. We run the model for six distinct LUC scenarios and computed the LUC-related GHG emission estimates given the changes in soil organic carbon (SOC) and biomass stocks, resulting in estimates with an associated uncertainty. We performed a statistical test to verify the existence of significant differences in the emission estimates between the scenarios and we run a sensitivity analysis to evaluate the contribution of the model components in the overall uncertainty of the emission estimates. The outcomes allows saying that adding uncertainty in the input data results in estimates with great uncertainty, specially in the emissions resulting from the changes in SOC stocks. The emission estimates obtained in this thesis have similar values when comparing to results of the deterministic approach of van der Hilst et al. (2018). The statistical test allows saying that the LUC-related GHG emission estimates resulting from an additional ethanol demand are significantly different between all scenarios, therefore the emission estimates could be used to support decision making e.g. to define or prioritize the implementation of a new LUC mitigation measure in Brazil.Verstegen, JudithHilst, Floor van derGranell-Canut, CarlosRUNBarroso, Renan Maron2019-04-24T10:29:08Z2019-02-042019-02-04T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/67515TID:202227669enginfo:eu-repo/semantics/openAccessreponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2024-05-22T17:38:56Zoai:run.unl.pt:10362/67515Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T17:09:59.979235Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiafalse
dc.title.none.fl_str_mv Evaluating the effects of uncertainty on projections of greenhouse gas emissions : a biofuel case study in Brazil
title Evaluating the effects of uncertainty on projections of greenhouse gas emissions : a biofuel case study in Brazil
spellingShingle Evaluating the effects of uncertainty on projections of greenhouse gas emissions : a biofuel case study in Brazil
Barroso, Renan Maron
Greenhouse gas emissions
Land use changes
Land use change projections
Mitigation measures
Brazil
Carbon stocks
Biofuel
Uncertainty
Stochastic modelling
Monte Carlo simulation
title_short Evaluating the effects of uncertainty on projections of greenhouse gas emissions : a biofuel case study in Brazil
title_full Evaluating the effects of uncertainty on projections of greenhouse gas emissions : a biofuel case study in Brazil
title_fullStr Evaluating the effects of uncertainty on projections of greenhouse gas emissions : a biofuel case study in Brazil
title_full_unstemmed Evaluating the effects of uncertainty on projections of greenhouse gas emissions : a biofuel case study in Brazil
title_sort Evaluating the effects of uncertainty on projections of greenhouse gas emissions : a biofuel case study in Brazil
author Barroso, Renan Maron
author_facet Barroso, Renan Maron
author_role author
dc.contributor.none.fl_str_mv Verstegen, Judith
Hilst, Floor van der
Granell-Canut, Carlos
RUN
dc.contributor.author.fl_str_mv Barroso, Renan Maron
dc.subject.por.fl_str_mv Greenhouse gas emissions
Land use changes
Land use change projections
Mitigation measures
Brazil
Carbon stocks
Biofuel
Uncertainty
Stochastic modelling
Monte Carlo simulation
topic Greenhouse gas emissions
Land use changes
Land use change projections
Mitigation measures
Brazil
Carbon stocks
Biofuel
Uncertainty
Stochastic modelling
Monte Carlo simulation
description Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial Technologies
publishDate 2019
dc.date.none.fl_str_mv 2019-04-24T10:29:08Z
2019-02-04
2019-02-04T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/67515
TID:202227669
url http://hdl.handle.net/10362/67515
identifier_str_mv TID:202227669
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron:RCAAP
instname_str FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron_str RCAAP
institution RCAAP
reponame_str Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
collection Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
repository.name.fl_str_mv Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
repository.mail.fl_str_mv info@rcaap.pt
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