Are conventional energy megaprojects competitive? Suboptimal decisions related to cost overruns in Brazil

Köberle, Alexandre C.; Garaffa, Rafael; Cunha, Bruno; Rochedo, Pedro; Lucena, André F.P.; Szklo, Alexandre; Schaeffer, Roberto

doi:10.1016/j.enpol.2018.08.021

Cited by 20 publications

(19 citation statements)

References 25 publications

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“…), even though there is no climate mitigation target. This can be explained by investment costs for large‐scale oil refineries, which are relatively expensive, as mentioned in Köberle et al . and Silverio .…”

Section: Resultsmentioning

confidence: 99%

“…The efficiency of the technologies is based on lower heating values obtained from the literature. The location factor used in this study for Brazil is 1.3 . The location factor is a factor that adds the costs of equipment and machinery that needs to be imported for the construction of (energy) projects .…”

Section: Methodsmentioning

confidence: 99%

“…A location factor of 1.3 means 30% extra costs. Hydropower plants built in Brazil require no imports of goods and therefore they have a location factor of 1 . Carbon dioxide emissions are attached to the conversion of energy carriers to secondary or final energy, and include process‐related emissions.…”

Section: Methodsmentioning

confidence: 99%

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Pathways for a Brazilian biobased economy: towards optimal utilization of biomass

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Köberle

et al. 2019

Biofuels Bioprod Bioref

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Biomass is responsible for 25% of the primary energy supply in Brazil. However, future biomass demand will be influenced by many factors. This study evaluates potential pathways for the utilization of biomass in Brazil until 2050, while considering novel biobased sectors (renewable jet fuel and biochemicals), resource competition, and greenhouse gas (GHG) emissions. Whereas other least‐cost optimization models assess biobased options to meet energy and chemicals demand in Brazil to a limited extent, this study provides a detailed breakdown of biomass feedstock, including an extensive portfolio of biomass conversion technologies. A least‐cost optimization model is used to assess the demand for energy and chemicals, and the competition between biomass and other climate‐mitigation measures such as renewable power generation technologies, carbon capture and storage (CCS), and energy efficiency. Varied over the three scenarios, 86–96% of the sustainable biomass supply potential is used. Under more stringent mitigation targets, novel biomass conversion technologies start to play an important role: Biobased electricity production with CCS, jet fuel production from lignocellulosic biomass, and chemicals are partly produced from ethanol and bio‐naphtha. The modeling framework provides a transparent view of which type of biomass can be used for which specific purpose. It is therefore an interesting tool for future research, for example to examine the dynamic interaction with demand for land. © 2019 The Authors. Biofuels, Bioproducts and Biorefining published by Society of Chemical Industry and John Wiley & Sons, Ltd.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Pathways for a Brazilian biobased economy: towards optimal utilization of biomass

Lap

Benders

Köberle

et al. 2019

Biofuels Bioprod Bioref

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“…We have tried to control this important issue by comparing our data to the data of other models in the major modeling effort of the CD-links project. Nevertheless, we do recognize that in Brazil location cost factors may be important, given the country's labor and capital productivity 92 . Moreover, we also acknowledge that energy megaprojects in Brazil have been facing overrun costs and construction delays 98 .…”

Section: Cost Of Technological Optionsmentioning

confidence: 86%

“…In terms of our own modelling effort, it is worth saying that we recognize the need to implement some probabilistic and risk analyses in our model 92 . However, this is not trivial, given the large level of detail in the technological representation and the high number of key variables within the BLUES model.…”

Section: Limitation Of the Study And Sensitivity Analysesmentioning

confidence: 99%