Uncertainty Analysis of Life Cycle Greenhouse Gas Emissions from Petroleum-Based Fuels and Impacts on Low Carbon Fuel Policies

Venkatesh, Aranya; Jaramillo, Paulina; Griffin, W. Michael; Matthews, H. Scott

doi:10.1021/es102498a

Cited by 81 publications

(49 citation statements)

References 22 publications

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“…From the results of the different case studies, more information was gained for decision making using the HDS approach compared to the DQI. The confidence level which is the important factor for decision making was observed, and it can be seen that the DQI approach gave more conservative results, consistent with conclusions in Venkatesh et al [34], Tan et al [21] and Lloyd and Ries [16], which could lead to unreliable decisions. For example, the results for all the case studies showed the pure DQI approach giving a 50 % probability making any decisions made using the pure DQI quite unreliable.…”

Section: Fig 5 A-h Results Of Tio 2 and Tio 3 Wind Turbinessupporting

confidence: 85%

Improving uncertainty analysis of embodied energy and embodied carbon in wind turbine design

Ozoemena

Cheung

Hasan

2017

Int J Adv Manuf Technol

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In this paper, a method for improving uncertainty estimates of embodied carbon and embodied energy is presented and discussed. Embodied energy and embodied carbon results are the focus of this analysis due to the fact that, at the conceptual design stage, these two are the most important quantities for decision making in life cycle assessment (LCA) studies. The use of renewable and new energy sources and the development of cleaner and more efficient energy technologies will play a major role in the sustainable development of a future energy strategy. Environmental protection, economic and social cohesion and diversification and security of energy supply are highlighted by the International Energy Agency as a high priority for the development of cleaner and more efficient energy systems and promotion of renewable energy sources. In the case studies presented, better results for the baseline turbine were observed compared to turbines with the proposed technology improvement opportunities. Embodied energy and embodied carbon results for the baseline turbine show an about 50 % probability that the turbine manufacturer may have lost the chance to reduce carbon emissions and 85 % probability that the turbine manufacturer may have lost the chance to reduce the primary energy consumed during its manufacture. The proposed approach is therefore a feasible alternative when more reliable results are desired for LCA-based design decision making.

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Section: Fig 5 A-h Results Of Tio 2 and Tio 3 Wind Turbinessupporting

confidence: 85%

Improving uncertainty analysis of embodied energy and embodied carbon in wind turbine design

Ozoemena

Cheung

Hasan

2017

Int J Adv Manuf Technol

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“…at the upper bounds. A similar observation has been made in other research works as well, where the semi-quantitative DQI approach overestimated the uncertainty [29,48,49]. In particular, the shape parameter and range endpoint appearing in the beta distribution may not be universally applicable; therefore, the beta distribution parameters (shape parameter and range endpoint) should be determined for the agricultural LCI dataset in the IDEA.…”

Section: Modification Of the Semi-quantitative Dqi Approach For The Asupporting

confidence: 77%

“…The alternative approach B solves the apparent underestimation of the uncertainty estimated by the conventional semi-quantitative DQI approaches by other researchers [29,48,49].…”

Section: Discussionmentioning

confidence: 99%

Parameter Uncertainty Analysis of the Life Cycle Inventory Database: Application to Greenhouse Gas Emissions from Brown Rice Production in IDEA

2018

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Abstract:The objective of this paper is to develop a simple method for analyzing the parameter uncertainty of the Japanese life cycle inventory database (LCI DB), termed the inventory database for environmental analysis (IDEA). The IDEA has a weakness of poor data quality because over 60% of datasets in IDEA were compiled based on secondary data (non-site-specific data sources). Three different approaches were used to estimate the uncertainty of the brown rice production dataset, including the stochastic modeling approach, the semi-quantitative DQI (Data Quality Indicator) approach, and a modification of the semi-quantitative DQI approach (including two alternative approaches for modification). The stochastic modeling approach provided the best estimate of the true mean of the sample space and its results were used as the reference for comparison with the other approaches. A simple method for the parameter uncertainty analysis of the agriculture industry DB was proposed by modifying the beta distribution parameters (endpoint range, shape parameter) in the semi-quantitative DQI approach using the results from the stochastic modeling approach. The effect of changing the beta distribution parameters in the semi-quantitative DQI approach indicated that the proposed method is an efficient method for the quantitative parameter uncertainty analysis of the brown rice production dataset in the IDEA.

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“…Through testing, we determined that 5,000 MC trials were sufficient to characterize the distributions of GHG emissions and freshwater consumption associated with the life cycle of Bakken crude, as well as individual life cycle phases. Emissions associated with combustion of gasoline and diesel were considered constant in these simulations; the coefficient of variation for these emissions has been reported to be 2% (46). Direct refinery GHG emissions per barrel of crude were also considered to be constant.…”

Section: Methodsmentioning

confidence: 99%

“…Additional data and figures of pertinence to the assessment of impacts associated with casings (54), cement, corrosion and scale inhibitors, hydraulic fracturing additives, artificial lift (operation of the pumping unit), fugitives (40), tank recirculation pumps (55), tank vapors (56), class II disposal of produced water, flaring due to infrastructural constraints (46), and crude transportation modes (13) and distances are provided in SI Appendix, section 4.1. XTO Energy uses the same service providers used by other oil and gas companies for drilling, hydraulic fracturing, and other upstream operations that directly generate GHG emissions.…”

Section: Methodsmentioning

confidence: 99%

Life cycle greenhouse gas emissions and freshwater consumption associated with Bakken tight oil

Laurenzi

Bergerson

Motazedi

2016

Proc. Natl. Acad. Sci. U.S.A.

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In recent years, hydraulic fracturing and horizontal drilling have been applied to extract crude oil from tight reservoirs, including the Bakken formation. There is growing interest in understanding the greenhouse gas (GHG) emissions associated with the development of tight oil. We conducted a life cycle assessment of Bakken crude using data from operations throughout the supply chain, including drilling and completion, refining, and use of refined products. If associated gas is gathered throughout the Bakken well life cycle, then the well to wheel GHG emissions are estimated to be 89 g CO 2 eq/MJ (80% CI, 87-94) of Bakken-derived gasoline and 90 g CO 2 eq/MJ (80% CI, 88-94) of diesel. If associated gas is flared for the first 12 mo of production, then life cycle GHG emissions increase by 5% on average. Regardless of the level of flaring, the Bakken life cycle GHG emissions are comparable to those of other crudes refined in the United States because flaring GHG emissions are largely offset at the refinery due to the physical properties of this tight oil. We also assessed the life cycle freshwater consumptions of Bakken-derived gasoline and diesel to be 1.14 (80% CI, 0.67-2.15) and 1.22 barrel/barrel (80% CI, 0.71-2.29), respectively, 13% of which is associated with hydraulic fracturing.life cycle assessment | unconventional resources | petroleum | hydraulic fracturing | flaring

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Uncertainty Analysis of Life Cycle Greenhouse Gas Emissions from Petroleum-Based Fuels and Impacts on Low Carbon Fuel Policies

Cited by 81 publications

References 22 publications

Improving uncertainty analysis of embodied energy and embodied carbon in wind turbine design

Improving uncertainty analysis of embodied energy and embodied carbon in wind turbine design

Parameter Uncertainty Analysis of the Life Cycle Inventory Database: Application to Greenhouse Gas Emissions from Brown Rice Production in IDEA

Life cycle greenhouse gas emissions and freshwater consumption associated with Bakken tight oil

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