scholarly journals A Protocol for the Global Sensitivity Analysis of Impact Assessment Models in Life Cycle Assessment

Risk Analysis ◽  
2015 ◽  
Vol 36 (2) ◽  
pp. 357-377 ◽  
Author(s):  
S. Cucurachi ◽  
E. Borgonovo ◽  
R. Heijungs
Keyword(s):  
Sensitivity Analysis ◽  
Life Cycle Assessment ◽  
Life Cycle ◽  
Impact Assessment ◽  
Global Sensitivity Analysis ◽  
Global Sensitivity

scholarly journals Methods for global sensitivity analysis in life cycle assessment

2016 ◽  
Vol 22 (7) ◽  
pp. 1125-1137 ◽  
Author(s):  
Evelyne A. Groen ◽  
Eddie A. M. Bokkers ◽  
Reinout Heijungs ◽  
Imke J. M. de Boer
Keyword(s):  
Sensitivity Analysis ◽  
Life Cycle Assessment ◽  
Life Cycle ◽  
Global Sensitivity Analysis ◽  
Global Sensitivity

Design of experiments for global sensitivity analysis in life cycle assessment: The case of biodiesel in Vietnam

2017 ◽  
Vol 119 ◽  
pp. 12-23 ◽  
Author(s):  
Dinh S. Khang ◽  
Raymond R. Tan ◽  
O. Manuel Uy ◽  
Michael Angelo B. Promentilla ◽  
Phan D. Tuan ◽  
...  
Keyword(s):  
Sensitivity Analysis ◽  
Life Cycle Assessment ◽  
Life Cycle ◽  
Design Of Experiments ◽  
Global Sensitivity Analysis ◽  
Global Sensitivity

scholarly journals Variance-based global sensitivity analysis and beyond in life cycle assessment: an application to geothermal heating networks

2021 ◽  
Author(s):  
Marc Jaxa-Rozen ◽  
Astu Sam Pratiwi ◽  
Evelina Trutnevyte
Keyword(s):  
Sensitivity Analysis ◽  
Life Cycle Assessment ◽  
Environmental Impacts ◽  
Spectral Clustering ◽  
Global Sensitivity Analysis ◽  
Sobol Indices ◽  
Global Sensitivity ◽  
Geothermal Heating ◽  
Trade Offs ◽  
Heating Networks

Abstract Purpose Global sensitivity analysis increasingly replaces manual sensitivity analysis in life cycle assessment (LCA). Variance-based global sensitivity analysis identifies influential uncertain model input parameters by estimating so-called Sobol indices that represent each parameter’s contribution to the variance in model output. However, this technique can potentially be unreliable when analyzing non-normal model outputs, and it does not inform analysts about specific values of the model input or output that may be decision-relevant. We demonstrate three emerging methods that build on variance-based global sensitivity analysis and that can provide new insights on uncertainty in typical LCA applications that present non-normal output distributions, trade-offs between environmental impacts, and interactions between model inputs. Methods To identify influential model inputs, trade-offs, and decision-relevant interactions, we implement techniques for distribution-based global sensitivity analysis (PAWN technique), spectral clustering, and scenario discovery (patient rule induction method: PRIM). We choose these techniques because they are applicable with generic Monte Carlo sampling and common LCA software. We compare these techniques with variance-based Sobol indices, using a previously published LCA case study of geothermal heating networks. We assess eight environmental impacts under uncertainty for three design alternatives, spanning different geothermal production temperatures and heating network configurations. Results In the application case on geothermal heating networks, PAWN distribution-based sensitivity indices generally identify influential model parameters consistently with Sobol indices. However, some discrepancies highlight the potentially misleading interpretation of Sobol indices on the non-normal distributions obtained in our analysis, where variance may not meaningfully describe uncertainty. Spectral clustering highlights groups of model results that present different trade-offs between environmental impacts. Compared to second-order Sobol interaction indices, PRIM then provides more precise information regarding the combinations of input values associated with these different groups of calculated impacts. PAWN indices, spectral clustering, and PRIM have a computational advantage because they yield stable results at relatively small sample sizes (n = 12,000), unlike Sobol indices (n = 100,000 for second-order indices). Conclusions We recommend adding these new techniques to global sensitivity analysis in LCA as they give more precise as well as additional insights on uncertainty regardless of the distribution of the model outputs. PAWN distribution-based global sensitivity analysis provides a computationally efficient assessment of input sensitivities as compared to variance-based global sensitivity analysis. The combination of clustering and scenario discovery enables analysts to precisely identify combinations of input parameters or uncertainties associated with different outcomes of environmental impacts.

A life cycle assessment of a ‘minus carbon’ refugee house: global warming potential and sensitivity analysis

2020 ◽  
Vol 14 (3) ◽  
pp. 559-579
Author(s):  
Marwa Dabaieh ◽  
Nargessadat Emami ◽  
Jukka Taneli Heinonen ◽  
Björn Marteinsson
Keyword(s):  
Sensitivity Analysis ◽  
Global Warming ◽  
Life Cycle Assessment ◽  
Life Cycle ◽  
Middle East ◽  
Impact Assessment ◽  
Global Warming Potential ◽  
Raw Materials ◽  
Proof Of Concept ◽  
Content Type

PurposeOver the last eight years, the Middle East has experienced a series of high profile conflicts which have resulted in over 5.6 million Syrians forced to migrate to neighbouring countries within the MENA (Middle East and North Africa) region or to Europe. That have exerted huge pressure on hosting countries trying to accommodate refugees in decent shelters and in quick manner. Temporary shelters normally carry a high environmental burden due to their short lifespan, and the majority are fabricated from industrialised materials. This study assesses the carbon impact for a minus carbon experimental refugee house in Sweden using life cycle assessment (LCA) as tool. SimaPro and GaBi software were used for the calculations and the ReCiPe midpoint method for impact assessment. The results show that using local plant-based materials such as straw, reeds and wood, together with clay dug from close to the construction site, can drastically reduce the carbon footprint of temporary shelters and even attain a negative carbon impact of 226.2 kg CO2 eq/m2. Based on the results of the uncertainty importance analysis, the overall global warming potential impact without and with sequestration potential are mostly sensitive to the variability of the GWP impact of wood fibre insulation.Design/methodology/approachThe methodology is designed to calculate the GWP impact of the refugee house over its entire life cycle (production, operation and maintenance and end of life). Then, the sensitivity analysis was performed to explore the impact of input uncertainties (selection of material from the database and the method) on the total GWP impact of the refugee house with and without sequestration. The ISO standards (International Standard 14040 2006; International Standard 14044 2006) divide the LCA framework into four steps of Goal and scope, inventory analysis, impact assessment, and interpretation.FindingsThis study has shown an example for proof of concept for a low impact refugee house prototype using straw, reeds, clay, lime and wood as the principle raw materials for building construction. Using natural materials, especially plant-based fibres, as the main construction materials, proved to achieve a minus carbon outcome over the life cycle of the building. The GWP of the shelter house without and with sequestration are found to be 254.7 kg CO2 eq/m2 and -226.2 kg CO2 eq/m2, respectively.Originality/valueAs there are still very few studies concerned with the environmental impact of temporary refugee housing, this study contributes to the pool of knowledge by introducing a complete LCA calculation for a physical house prototype as a proof of concept on how using low impact raw materials for construction combined with passive solutions for heating and cooling can reach a minus carbon outcome. The GWP of the shelter house without and with sequestration are found to be 254.7 kg CO2 eq/m2 and -226.2 kg CO2 eq/m2.

scholarly journals Life Cycle Assessment of Renewable Polyol Monomers for Polyurethane Production

2020 ◽  
Author(s):  
◽  
Anda Fridrihsone
Keyword(s):  
Sensitivity Analysis ◽  
Life Cycle Assessment ◽  
Life Cycle ◽  
Impact Assessment ◽  
Energy Demand ◽  
Rapeseed Oil ◽  
Polyol Synthesis ◽  
Impact Indicator ◽  
Rapeseed Production ◽  
Allocation Methods

ycle Inventory of winter and spring rapeseed production in Latvia as a case study country in Northern Europe. In-depth and up-to-date agricultural practices used in the region under study data were provided by a large agricultural company located in Zemgale region in Latvia. Chapter III Part II presents an inventory of rapeseed oil mill stage with data provided by operation oil mill in Zemgale region. Different allocation methods are applied to further investigate the effect of different allocation methods on the environmental profiles of rapeseed oil-based bio-polyols and the consequences of applying these methods considering the main aims of the study. Chapter III Part III presents Life Cycle Impact Assessment of winter and spring rapeseed production and rapeseed oil production. Environmental performance is analyzed with Cumulative energy demand impact indicator along with the ReCiPe impact assessment methodology. The main environmental hotspots were identified. Sensitivity analysis was performed. Chapter III Part IV presents results of the Life Cycle Assessment of two rapeseed oil-based bio-polyols. Life Cycle Inventories were built on experimental data for polyol synthesis that were performed in a pilot-scale (50 L reactor). Bio-based polyols were compared with the petrochemical counterpart. The two developed rapeseed oil-based polyols were analyzed with three different modelling approaches for the bio-based feedstock stage. Sensitivity analysis was performed.

Sign in / Sign up

Export Citation Format

Share Document