Numerical Approach for Generating Beta Random Variates

Riggs, Leland S.

doi:10.1061/(asce)0887-3801(1989)3:2(183)

Cited by 7 publications

(4 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This spatial variability is calculated based on the CF value of each native region weighted by the probability of the environmental intervention to occur in each native region as described in Bulle et al (2019). It is represented with a four-parameter beta distribution using the moment method (Riggs 1989) to preserve at least the 4 parameters we knew for each CF: minimum and maximum values, variance and mean values. Four-parameter beta distribution is an alternative parametrization of the standard beta distribution (with 2 parameters) with a support of [min;max] instead of [0;1] and was the best fit we found compared to more classical distributions (normal, triangle, log-normal).…”

Section: Relative Spatial Variation Of Pricesmentioning

confidence: 99%

Prioritizing regionalization to enhance interpretation in consequential life cycle assessment: application to alternative transportation scenarios using partial equilibrium economic modeling

Patouillard

Lorne

Collet

et al. 2020

Int J Life Cycle Assess

View full text Add to dashboard Cite

The case study is defined in the context of the French law on the energy transition (LTECV: Loi relative à la Transition Energétique pour la Croissance Verte, referred to as LTE here) published in August 2015 (Assemblée nationale and Sénat de France 2015). The law proposes a set of targets to tackle climate change, preserve the environment and enhance energy independence in France. In the transport sector, the main objective of the LTE is to "develop clean transports to enhance the air quality and protect the health of French people". Its implementation is done with two targets: (i) 15% renewable energy consumption in the transport sector and (ii) a reduction of 30% in fossil primary energy consumption by 2030 as compared to the 2012 level.

show abstract

Section: Relative Spatial Variation Of Pricesmentioning

confidence: 99%

Prioritizing regionalization to enhance interpretation in consequential life cycle assessment: application to alternative transportation scenarios using partial equilibrium economic modeling

Patouillard

Lorne

Collet

et al. 2020

Int J Life Cycle Assess

View full text Add to dashboard Cite

show abstract

“…The spatial variability of global CFs is represented with a fourparameter beta distribution, which has definite lower and upper bounds and fits a variety of shapes. We used the moment method described in Riggs (1989) to estimate the four parameters of each CF distribution. During the Monte Carlo sampling, we also accounted for the LCIA spatial correlation in a unit process for land transformation IC only and only for some EFs, i.e., sampled value for CF from one type of land use is consistent with the sampled value for CF to one type of land use.…”

Section: Data and Settings Used For The Case Studiesmentioning

confidence: 99%

Prioritizing regionalization efforts in life cycle assessment through global sensitivity analysis: a sector meta-analysis based on ecoinvent v3

Patouillard

Collet

Lesage

et al. 2019

Int J Life Cycle Assess

View full text Add to dashboard Cite

Purpose Regionalization in life cycle assessment (LCA) aims to increase the representativeness of LCA results and reduce the uncertainty due to spatial variability. It may refer to adapting processes to better account for regional technological specificities (inventory regionalization) or adding of spatial information to the elementary flows (inventory spatialization) which allow using more regionalized characterization factors. However, developing and integrating regionalization requires additional efforts for LCA practitioners and database developers that must be prioritized. Methods We propose a stepwise methodology for LCA practitioners to prioritize data collection for regionalization based on global sensitivity analysis (GSA) using Sobol indices. It involves several GSA to select the impact categories (ICs) that require further inventory data collection (IC ranking), prioritize between inventory regionalization and inventory spatialization (LCA phase ranking), and target specific data to collect. Then we propose a method to derive sector-specific recommendations using statistical tests to prioritize inventory regionalization versus spatialization and the ICs on which to focus inventory data collection. These recommendations are meant to help LCA practitioners and database developers define their strategy for regional data collection by focusing on data that have the highest potential to reduce the uncertainty of the results. Results and discussionThe applicability of the methodology is illustrated through three case studies using the ecoinvent v3 database and the regionalized impact methodology IMPACT World+: one on prioritizing data collection in a single biofuel product system and two meta-analyses of all product systems in two distinct economic sectors (biofuel production and land passenger transport). Recommendations for regionalization can be derived for an economic sector and appear to be different from one economic sector to another. GSA seems to be more relevant to prioritize regionalization efforts than an impact contribution analysis (ICA) approach often used to prioritize data collection in LCA. However, further improvements, such as accounting for spatial correlations and better computational times for GSA, are required to implement it in LCA. Conclusions We recommend using the methodology based on GSA to efficiently prioritize regionalization efforts between ICs and between inventory regionalization and inventory spatialization. We proved that the implementation of IC ranking and LCA phase ranking is computationally feasible and therefore invite current LCA software providers to unlock this new horizon in LCA interpretation. We also invite to expand the meta-analysis to all sectors in an LCA database.

show abstract

“…Previous research has shown that beta distribution is suitable for modelling cost uncertainty in construction projects (e.g., Riggs, 1989;Abou-Rikz et al,1993). It can be used to model events that are constrained to take place within an interval defined by a minimum and maximum value and therefore is often used in scheduling to describe the time of completion and the cost of a task.…”

Section: Distribution Fittingmentioning

confidence: 99%

The costs of rework: insights from construction and opportunities for learning

Love

Smith

Ackermann

et al. 2018

Production Planning & Control

View full text Add to dashboard Cite

During the production of artefacts in construction there is a likelihood for errors to be committed, which may need to be rectified so that they conform to defined contractual requirements and standards. In doing so, this initiates a process of rework, which is a problem that the industry has incessantly aimed to redress for decades with limited success. Rework is a 'known-unknown', but there remains a high degree of uncertainty about its costs. Such uncertainty occurs as there is a proclivity for the costs associated with rework to be largely ignored, concealed or considered to be normal function of operations. This paper presents the results from the first longitudinal and in-depth study of rework costs in construction. Based on a sample of 19,605 rework events derived from 346 construction projects delivered by a contractor between the years 2009 and 2015, it was revealed that their mean yearly profit over the period of analysis was reduced by a staggering 28%. In addition, 88 (0.45%) of the total 19,605 rework events accounted for 34% of the total costs that were incurred. The complete cost data for 98 of the 346 projects was made available, which enabled a mean rework cost of 0.39% of contract value to be determined. The research provides construction organisations with an improved understanding of the nature and likelihood of rework costs enabling them to move from a position of being a 'known-unknown' to becoming a 'knownknown'. Being able to 'anticipate what might go wrong' and ensure that risk management and controls are put in place throughout the construction process will contribute to their dynamic capability.

show abstract

Numerical Approach for Generating Beta Random Variates

Cited by 7 publications

References 1 publication

Prioritizing regionalization to enhance interpretation in consequential life cycle assessment: application to alternative transportation scenarios using partial equilibrium economic modeling

Prioritizing regionalization to enhance interpretation in consequential life cycle assessment: application to alternative transportation scenarios using partial equilibrium economic modeling

Prioritizing regionalization efforts in life cycle assessment through global sensitivity analysis: a sector meta-analysis based on ecoinvent v3

The costs of rework: insights from construction and opportunities for learning

Contact Info

Product

Resources

About