Uncertainty of greenhouse gas emission models: A case in Colombia’s transport sector

Valenzuela, Manuela M.; Espinosa, Mónica; Virgüez, Edgar; Behrentz, Eduardo

doi:10.1016/j.trpro.2017.05.380

Cited by 7 publications

(4 citation statements)

References 13 publications

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“…The uniform (rectangular) distribution, defined by a lower limit (a) and an upper limit (b) within a plausible range (U: a, b), signifies equal probability between these endpoints. This distribution is suitable when information is only available for the lower and upper limit values [32]. The triangular probability distribution (T: a, b, c) is continuous and characterized by a lower limit (a), an upper limit (b), and the most plausible estimate (c).…”

Section: Developing Input Distributionsmentioning

confidence: 99%

Greenhouse Gas Emissions Performance of Electric, Hydrogen and Fossil-Fuelled Freight Trucks with Uncertainty Estimates Using a Probabilistic Life-Cycle Assessment (pLCA)

Smit,

Helmers,

Schwingshackl

et al. 2024

Sustainability

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This research conducted a probabilistic life-cycle assessment (pLCA) into the greenhouse gas (GHG) emissions performance of nine combinations of truck size and powertrain technology for a recent past and a future (largely decarbonised) situation in Australia. This study finds that the relative and absolute life-cycle GHG emissions performance strongly depends on the vehicle class, powertrain and year of assessment. Life-cycle emission factor distributions vary substantially in their magnitude, range and shape. Diesel trucks had lower life-cycle GHG emissions in 2019 than electric trucks (battery, hydrogen fuel cell), mainly due to the high carbon-emission intensity of the Australian electricity grid (mainly coal) and hydrogen production (mainly through steam–methane reforming). The picture is, however, very different for a more decarbonised situation, where battery electric trucks, in particular, provide deep reductions (about 75–85%) in life-cycle GHG emissions. Fuel-cell electric (hydrogen) trucks also provide substantial reductions (about 50–70%), but not as deep as those for battery electric trucks. Moreover, hydrogen trucks exhibit the largest uncertainty in emissions performance, which reflects the uncertainty and general lack of information for this technology. They therefore carry an elevated risk of not achieving the expected emission reductions. Battery electric trucks show the smallest (absolute) uncertainty, which suggests that these trucks are expected to deliver the deepest and most robust emission reductions. Operational emissions (on-road driving and vehicle maintenance combined) dominate life-cycle emissions for all vehicle classes. Vehicle manufacturing and upstream emissions make a relatively small contribution to life-cycle emissions from diesel trucks (<5% each), but these are important aspects for electric trucks (5% to 30%).

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Section: Developing Input Distributionsmentioning

confidence: 99%

Greenhouse Gas Emissions Performance of Electric, Hydrogen and Fossil-Fuelled Freight Trucks with Uncertainty Estimates Using a Probabilistic Life-Cycle Assessment (pLCA)

Smit,

Helmers,

Schwingshackl

et al. 2024

Sustainability

View full text Add to dashboard Cite

show abstract

“…A uniform distribution represents equal probability between two end points. This distribution is appropriate if only information on the lower and upper limit values are available [40]. The triangular probability distribution (T: a, b, c) is a continuous probability distribution, which is defined by a lower limit a and an upper limit b of a plausible range, as well as the most plausible estimate c. The triangular distribution is appropriate for situations in which the exact form of a distribution is not precisely known, but in which values toward the middle of the range of possible values are considered more likely to occur than values near either extreme [41].…”

Section: Input Distribution Developmentmentioning

confidence: 99%

Greenhouse Gas Emissions Performance of Electric and Fossil-Fueled Passenger Vehicles with Uncertainty Estimates Using a Probabilistic Life-Cycle Assessment

Smit¹,

Kennedy²

2022

Sustainability

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A technology assessment is conducted for battery electric and conventional fossil-fueled passenger vehicles for three Australian scenarios and seven Australian states and territories. This study uses a probabilistic life-cycle assessment (pLCA) to explicitly quantify uncertainty in the LCA inputs and results. Parametric input distributions are developed using statistical techniques. For the 2018 Australian electricity mix, which is still largely fossil fuels based, the weight of evidence suggests that electric vehicles will reduce GHG emission rates by 29% to 41%. For the ‘fossil fuels only’ marginal electricity scenario, electric vehicles are still expected to significantly reduce emission rates by between 10% and 32%. Large reductions between 74% and 80% are observed for the more renewables scenario. For the Australian jurisdictions, the average LCA GHG emission factors vary substantially for conventional vehicles (364–390 g CO2-e/km), but particularly for electric vehicles (98–287 g CO2-e/km), which reflects the differences in fuel mix for electricity generation in the different states and territories. Electrification of the Tasmanian on-road fleet has the largest predicted fleet average reduction in LCA greenhouse gas emissions of 243–300 g CO2-e/km. A sensitivity analysis with alternative input distributions suggests that the outcomes from this study are robust.

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“…For the transport sector, Valenzuela et al [45] developed a model to estimate the GHG emissions generated by that sector in 2010. In this model, national emissions were projected for the 2010-2040 period, using Kaya factors.…”

Section: Co 2 Reduction In the Distribution Of Perishable Products Inmentioning

confidence: 99%

“…According to Valenzuela et al [45], efforts should be made for perishable products to travel shorter distances and to help reduce carbon emissions, which are the main driver of climate change.…”

Section: Co 2 Reduction In the Distribution Of Perishable Products Inmentioning

confidence: 99%

Vehicles Allocation for Fruit Distribution Considering CO2 Emissions and Decisions on Subcontracting

2018

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An important problem in rural-area supply chains is how to transport the harvested fruit to urban areas. Low-and medium-capacity vehicles are used in Colombia to carry out this activity. Operating them comes with an inherent cost and generates carbon emissions. Normally, minimizing operating costs and minimizing carbon emissions are conflicting objectives to allocate such vehicles efficiently in any of the supply chain echelons. We designed a multi-objective mixed-integer programming model to address this problem and solved it via the ε-constraint method. It includes decisions mainly about quantities of fruit to transport and store, types of vehicles to allocate according to their capacities, CO 2 emission levels of these vehicles, and subcontracting on the collection process. The main results show two schedules for allocating the vehicles, showing minimum and maximum CO 2 emissions. Minimum CO 2 emissions scheme require subcontracting and the maximum CO 2 scheme does not. Then, a Pareto frontier shows that CO 2 emissions level are inversely proportional to total management cost for different scenarios in which fruit supply was modified.

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Uncertainty of greenhouse gas emission models: A case in Colombia’s transport sector

Cited by 7 publications

References 13 publications

Greenhouse Gas Emissions Performance of Electric, Hydrogen and Fossil-Fuelled Freight Trucks with Uncertainty Estimates Using a Probabilistic Life-Cycle Assessment (pLCA)

Greenhouse Gas Emissions Performance of Electric, Hydrogen and Fossil-Fuelled Freight Trucks with Uncertainty Estimates Using a Probabilistic Life-Cycle Assessment (pLCA)

Greenhouse Gas Emissions Performance of Electric and Fossil-Fueled Passenger Vehicles with Uncertainty Estimates Using a Probabilistic Life-Cycle Assessment

Vehicles Allocation for Fruit Distribution Considering CO2 Emissions and Decisions on Subcontracting

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