On the use of weighting in LCA: translating decision makers’ preferences into weights via linear programming
Purpose The main goal of any life cycle assessment (LCA) study is to identify solutions leading to environmental savings. In conventional LCA studies, practitioners select from some alternatives the one which better matches their preferences. This task is sometimes simplified by ranking these altern...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Cortés-Borda, Daniel [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2013 |
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| Anmerkung: |
© Springer-Verlag Berlin Heidelberg 2013 |
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| Übergeordnetes Werk: |
Enthalten in: The international journal of life cycle assessment - Springer-Verlag, 1996, 18(2013), 5 vom: 23. Jan., Seite 948-957 |
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| Übergeordnetes Werk: |
volume:18 ; year:2013 ; number:5 ; day:23 ; month:01 ; pages:948-957 |
| Links: |
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| DOI / URN: |
10.1007/s11367-012-0540-6 |
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| Katalog-ID: |
OLC2051198950 |
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| 520 | |a Purpose The main goal of any life cycle assessment (LCA) study is to identify solutions leading to environmental savings. In conventional LCA studies, practitioners select from some alternatives the one which better matches their preferences. This task is sometimes simplified by ranking these alternatives using an aggregated indicator defined by attaching weights to impacts. We address here the inverse problem. That is, given an alternative, we aim to determine the weights for which that solution becomes optimal. Methods We propose a method based on linear programming (LP) that determines, for a given alternative, the ranges within which the weights attached to a set of impact metrics must lie so that when a weighting combination of these impacts is optimized, the alternative can be optimal, while if the weights fall outside this range, it is guaranteed that the solution will be suboptimal. A large weight value implies that the corresponding LCA impact is given more importance, while a low value implies the converse. Furthermore, we provide a rigorous mathematical analysis on the implications of using weighting schemes in LCA, showing that this practice guides decision-making towards the adoption of some specific alternatives (those lying on the convex envelope of the resulting trade-off curve). Results and discussion A case study based on the design of hydrogen infrastructures is taken as a test bed to illustrate the capabilities of the approach presented. Given are a set of production and storage technologies available to produce and deliver hydrogen, a final demand, and cost and environmental data. A set of designs, each achieving a unique combination of cost and LCA impact, is considered. For each of them, we calculate the minimum and maximum weight to be given to every LCA impact so that the alternative can be optimal among all the candidate designs. Numerical results show that solutions with lower impact are selected when decision makers are willing to pay larger monetary penalties for the environmental damage caused. Conclusions LP can be used in LCA to translate the decision makers’ preferences into weights. This information is rather valuable, particularly when these weights represent economic penalties, as it allows screening and ranking alternatives on the basis of a common economic basis. Our framework is aimed at facilitating decision making in LCA studies and defines a general framework for comparing alternatives that show different performance in a wide variety of impact metrics. | ||
| 650 | 4 | |a Hydrogen supply chains | |
| 650 | 4 | |a Linear programming | |
| 650 | 4 | |a Pareto optimality | |
| 650 | 4 | |a Weighting | |
| 700 | 1 | |a Guillén-Gosálbez, Gonzalo |4 aut | |
| 700 | 1 | |a Esteller, Laureano Jiménez |4 aut | |
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10.1007/s11367-012-0540-6 doi (DE-627)OLC2051198950 (DE-He213)s11367-012-0540-6-p DE-627 ger DE-627 rakwb eng 650 330 333.7 VZ 690 VZ Cortés-Borda, Daniel verfasserin aut On the use of weighting in LCA: translating decision makers’ preferences into weights via linear programming 2013 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag Berlin Heidelberg 2013 Purpose The main goal of any life cycle assessment (LCA) study is to identify solutions leading to environmental savings. In conventional LCA studies, practitioners select from some alternatives the one which better matches their preferences. This task is sometimes simplified by ranking these alternatives using an aggregated indicator defined by attaching weights to impacts. We address here the inverse problem. That is, given an alternative, we aim to determine the weights for which that solution becomes optimal. Methods We propose a method based on linear programming (LP) that determines, for a given alternative, the ranges within which the weights attached to a set of impact metrics must lie so that when a weighting combination of these impacts is optimized, the alternative can be optimal, while if the weights fall outside this range, it is guaranteed that the solution will be suboptimal. A large weight value implies that the corresponding LCA impact is given more importance, while a low value implies the converse. Furthermore, we provide a rigorous mathematical analysis on the implications of using weighting schemes in LCA, showing that this practice guides decision-making towards the adoption of some specific alternatives (those lying on the convex envelope of the resulting trade-off curve). Results and discussion A case study based on the design of hydrogen infrastructures is taken as a test bed to illustrate the capabilities of the approach presented. Given are a set of production and storage technologies available to produce and deliver hydrogen, a final demand, and cost and environmental data. A set of designs, each achieving a unique combination of cost and LCA impact, is considered. For each of them, we calculate the minimum and maximum weight to be given to every LCA impact so that the alternative can be optimal among all the candidate designs. Numerical results show that solutions with lower impact are selected when decision makers are willing to pay larger monetary penalties for the environmental damage caused. Conclusions LP can be used in LCA to translate the decision makers’ preferences into weights. This information is rather valuable, particularly when these weights represent economic penalties, as it allows screening and ranking alternatives on the basis of a common economic basis. Our framework is aimed at facilitating decision making in LCA studies and defines a general framework for comparing alternatives that show different performance in a wide variety of impact metrics. Hydrogen supply chains Linear programming Pareto optimality Weighting Guillén-Gosálbez, Gonzalo aut Esteller, Laureano Jiménez aut Enthalten in The international journal of life cycle assessment Springer-Verlag, 1996 18(2013), 5 vom: 23. Jan., Seite 948-957 (DE-627)211584533 (DE-600)1319419-7 (DE-576)059728728 0948-3349 nnns volume:18 year:2013 number:5 day:23 month:01 pages:948-957 https://doi.org/10.1007/s11367-012-0540-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-UMW SSG-OLC-ARC SSG-OLC-TEC SSG-OPC-FOR GBV_ILN_30 GBV_ILN_70 GBV_ILN_267 GBV_ILN_2014 GBV_ILN_2016 GBV_ILN_2018 GBV_ILN_4012 GBV_ILN_4046 AR 18 2013 5 23 01 948-957 |
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10.1007/s11367-012-0540-6 doi (DE-627)OLC2051198950 (DE-He213)s11367-012-0540-6-p DE-627 ger DE-627 rakwb eng 650 330 333.7 VZ 690 VZ Cortés-Borda, Daniel verfasserin aut On the use of weighting in LCA: translating decision makers’ preferences into weights via linear programming 2013 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag Berlin Heidelberg 2013 Purpose The main goal of any life cycle assessment (LCA) study is to identify solutions leading to environmental savings. In conventional LCA studies, practitioners select from some alternatives the one which better matches their preferences. This task is sometimes simplified by ranking these alternatives using an aggregated indicator defined by attaching weights to impacts. We address here the inverse problem. That is, given an alternative, we aim to determine the weights for which that solution becomes optimal. Methods We propose a method based on linear programming (LP) that determines, for a given alternative, the ranges within which the weights attached to a set of impact metrics must lie so that when a weighting combination of these impacts is optimized, the alternative can be optimal, while if the weights fall outside this range, it is guaranteed that the solution will be suboptimal. A large weight value implies that the corresponding LCA impact is given more importance, while a low value implies the converse. Furthermore, we provide a rigorous mathematical analysis on the implications of using weighting schemes in LCA, showing that this practice guides decision-making towards the adoption of some specific alternatives (those lying on the convex envelope of the resulting trade-off curve). Results and discussion A case study based on the design of hydrogen infrastructures is taken as a test bed to illustrate the capabilities of the approach presented. Given are a set of production and storage technologies available to produce and deliver hydrogen, a final demand, and cost and environmental data. A set of designs, each achieving a unique combination of cost and LCA impact, is considered. For each of them, we calculate the minimum and maximum weight to be given to every LCA impact so that the alternative can be optimal among all the candidate designs. Numerical results show that solutions with lower impact are selected when decision makers are willing to pay larger monetary penalties for the environmental damage caused. Conclusions LP can be used in LCA to translate the decision makers’ preferences into weights. This information is rather valuable, particularly when these weights represent economic penalties, as it allows screening and ranking alternatives on the basis of a common economic basis. Our framework is aimed at facilitating decision making in LCA studies and defines a general framework for comparing alternatives that show different performance in a wide variety of impact metrics. Hydrogen supply chains Linear programming Pareto optimality Weighting Guillén-Gosálbez, Gonzalo aut Esteller, Laureano Jiménez aut Enthalten in The international journal of life cycle assessment Springer-Verlag, 1996 18(2013), 5 vom: 23. Jan., Seite 948-957 (DE-627)211584533 (DE-600)1319419-7 (DE-576)059728728 0948-3349 nnns volume:18 year:2013 number:5 day:23 month:01 pages:948-957 https://doi.org/10.1007/s11367-012-0540-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-UMW SSG-OLC-ARC SSG-OLC-TEC SSG-OPC-FOR GBV_ILN_30 GBV_ILN_70 GBV_ILN_267 GBV_ILN_2014 GBV_ILN_2016 GBV_ILN_2018 GBV_ILN_4012 GBV_ILN_4046 AR 18 2013 5 23 01 948-957 |
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10.1007/s11367-012-0540-6 doi (DE-627)OLC2051198950 (DE-He213)s11367-012-0540-6-p DE-627 ger DE-627 rakwb eng 650 330 333.7 VZ 690 VZ Cortés-Borda, Daniel verfasserin aut On the use of weighting in LCA: translating decision makers’ preferences into weights via linear programming 2013 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag Berlin Heidelberg 2013 Purpose The main goal of any life cycle assessment (LCA) study is to identify solutions leading to environmental savings. In conventional LCA studies, practitioners select from some alternatives the one which better matches their preferences. This task is sometimes simplified by ranking these alternatives using an aggregated indicator defined by attaching weights to impacts. We address here the inverse problem. That is, given an alternative, we aim to determine the weights for which that solution becomes optimal. Methods We propose a method based on linear programming (LP) that determines, for a given alternative, the ranges within which the weights attached to a set of impact metrics must lie so that when a weighting combination of these impacts is optimized, the alternative can be optimal, while if the weights fall outside this range, it is guaranteed that the solution will be suboptimal. A large weight value implies that the corresponding LCA impact is given more importance, while a low value implies the converse. Furthermore, we provide a rigorous mathematical analysis on the implications of using weighting schemes in LCA, showing that this practice guides decision-making towards the adoption of some specific alternatives (those lying on the convex envelope of the resulting trade-off curve). Results and discussion A case study based on the design of hydrogen infrastructures is taken as a test bed to illustrate the capabilities of the approach presented. Given are a set of production and storage technologies available to produce and deliver hydrogen, a final demand, and cost and environmental data. A set of designs, each achieving a unique combination of cost and LCA impact, is considered. For each of them, we calculate the minimum and maximum weight to be given to every LCA impact so that the alternative can be optimal among all the candidate designs. Numerical results show that solutions with lower impact are selected when decision makers are willing to pay larger monetary penalties for the environmental damage caused. Conclusions LP can be used in LCA to translate the decision makers’ preferences into weights. This information is rather valuable, particularly when these weights represent economic penalties, as it allows screening and ranking alternatives on the basis of a common economic basis. Our framework is aimed at facilitating decision making in LCA studies and defines a general framework for comparing alternatives that show different performance in a wide variety of impact metrics. Hydrogen supply chains Linear programming Pareto optimality Weighting Guillén-Gosálbez, Gonzalo aut Esteller, Laureano Jiménez aut Enthalten in The international journal of life cycle assessment Springer-Verlag, 1996 18(2013), 5 vom: 23. Jan., Seite 948-957 (DE-627)211584533 (DE-600)1319419-7 (DE-576)059728728 0948-3349 nnns volume:18 year:2013 number:5 day:23 month:01 pages:948-957 https://doi.org/10.1007/s11367-012-0540-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-UMW SSG-OLC-ARC SSG-OLC-TEC SSG-OPC-FOR GBV_ILN_30 GBV_ILN_70 GBV_ILN_267 GBV_ILN_2014 GBV_ILN_2016 GBV_ILN_2018 GBV_ILN_4012 GBV_ILN_4046 AR 18 2013 5 23 01 948-957 |
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10.1007/s11367-012-0540-6 doi (DE-627)OLC2051198950 (DE-He213)s11367-012-0540-6-p DE-627 ger DE-627 rakwb eng 650 330 333.7 VZ 690 VZ Cortés-Borda, Daniel verfasserin aut On the use of weighting in LCA: translating decision makers’ preferences into weights via linear programming 2013 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag Berlin Heidelberg 2013 Purpose The main goal of any life cycle assessment (LCA) study is to identify solutions leading to environmental savings. In conventional LCA studies, practitioners select from some alternatives the one which better matches their preferences. This task is sometimes simplified by ranking these alternatives using an aggregated indicator defined by attaching weights to impacts. We address here the inverse problem. That is, given an alternative, we aim to determine the weights for which that solution becomes optimal. Methods We propose a method based on linear programming (LP) that determines, for a given alternative, the ranges within which the weights attached to a set of impact metrics must lie so that when a weighting combination of these impacts is optimized, the alternative can be optimal, while if the weights fall outside this range, it is guaranteed that the solution will be suboptimal. A large weight value implies that the corresponding LCA impact is given more importance, while a low value implies the converse. Furthermore, we provide a rigorous mathematical analysis on the implications of using weighting schemes in LCA, showing that this practice guides decision-making towards the adoption of some specific alternatives (those lying on the convex envelope of the resulting trade-off curve). Results and discussion A case study based on the design of hydrogen infrastructures is taken as a test bed to illustrate the capabilities of the approach presented. Given are a set of production and storage technologies available to produce and deliver hydrogen, a final demand, and cost and environmental data. A set of designs, each achieving a unique combination of cost and LCA impact, is considered. For each of them, we calculate the minimum and maximum weight to be given to every LCA impact so that the alternative can be optimal among all the candidate designs. Numerical results show that solutions with lower impact are selected when decision makers are willing to pay larger monetary penalties for the environmental damage caused. Conclusions LP can be used in LCA to translate the decision makers’ preferences into weights. This information is rather valuable, particularly when these weights represent economic penalties, as it allows screening and ranking alternatives on the basis of a common economic basis. Our framework is aimed at facilitating decision making in LCA studies and defines a general framework for comparing alternatives that show different performance in a wide variety of impact metrics. Hydrogen supply chains Linear programming Pareto optimality Weighting Guillén-Gosálbez, Gonzalo aut Esteller, Laureano Jiménez aut Enthalten in The international journal of life cycle assessment Springer-Verlag, 1996 18(2013), 5 vom: 23. Jan., Seite 948-957 (DE-627)211584533 (DE-600)1319419-7 (DE-576)059728728 0948-3349 nnns volume:18 year:2013 number:5 day:23 month:01 pages:948-957 https://doi.org/10.1007/s11367-012-0540-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-UMW SSG-OLC-ARC SSG-OLC-TEC SSG-OPC-FOR GBV_ILN_30 GBV_ILN_70 GBV_ILN_267 GBV_ILN_2014 GBV_ILN_2016 GBV_ILN_2018 GBV_ILN_4012 GBV_ILN_4046 AR 18 2013 5 23 01 948-957 |
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10.1007/s11367-012-0540-6 doi (DE-627)OLC2051198950 (DE-He213)s11367-012-0540-6-p DE-627 ger DE-627 rakwb eng 650 330 333.7 VZ 690 VZ Cortés-Borda, Daniel verfasserin aut On the use of weighting in LCA: translating decision makers’ preferences into weights via linear programming 2013 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag Berlin Heidelberg 2013 Purpose The main goal of any life cycle assessment (LCA) study is to identify solutions leading to environmental savings. In conventional LCA studies, practitioners select from some alternatives the one which better matches their preferences. This task is sometimes simplified by ranking these alternatives using an aggregated indicator defined by attaching weights to impacts. We address here the inverse problem. That is, given an alternative, we aim to determine the weights for which that solution becomes optimal. Methods We propose a method based on linear programming (LP) that determines, for a given alternative, the ranges within which the weights attached to a set of impact metrics must lie so that when a weighting combination of these impacts is optimized, the alternative can be optimal, while if the weights fall outside this range, it is guaranteed that the solution will be suboptimal. A large weight value implies that the corresponding LCA impact is given more importance, while a low value implies the converse. Furthermore, we provide a rigorous mathematical analysis on the implications of using weighting schemes in LCA, showing that this practice guides decision-making towards the adoption of some specific alternatives (those lying on the convex envelope of the resulting trade-off curve). Results and discussion A case study based on the design of hydrogen infrastructures is taken as a test bed to illustrate the capabilities of the approach presented. Given are a set of production and storage technologies available to produce and deliver hydrogen, a final demand, and cost and environmental data. A set of designs, each achieving a unique combination of cost and LCA impact, is considered. For each of them, we calculate the minimum and maximum weight to be given to every LCA impact so that the alternative can be optimal among all the candidate designs. Numerical results show that solutions with lower impact are selected when decision makers are willing to pay larger monetary penalties for the environmental damage caused. Conclusions LP can be used in LCA to translate the decision makers’ preferences into weights. This information is rather valuable, particularly when these weights represent economic penalties, as it allows screening and ranking alternatives on the basis of a common economic basis. Our framework is aimed at facilitating decision making in LCA studies and defines a general framework for comparing alternatives that show different performance in a wide variety of impact metrics. Hydrogen supply chains Linear programming Pareto optimality Weighting Guillén-Gosálbez, Gonzalo aut Esteller, Laureano Jiménez aut Enthalten in The international journal of life cycle assessment Springer-Verlag, 1996 18(2013), 5 vom: 23. Jan., Seite 948-957 (DE-627)211584533 (DE-600)1319419-7 (DE-576)059728728 0948-3349 nnns volume:18 year:2013 number:5 day:23 month:01 pages:948-957 https://doi.org/10.1007/s11367-012-0540-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-UMW SSG-OLC-ARC SSG-OLC-TEC SSG-OPC-FOR GBV_ILN_30 GBV_ILN_70 GBV_ILN_267 GBV_ILN_2014 GBV_ILN_2016 GBV_ILN_2018 GBV_ILN_4012 GBV_ILN_4046 AR 18 2013 5 23 01 948-957 |
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In conventional LCA studies, practitioners select from some alternatives the one which better matches their preferences. This task is sometimes simplified by ranking these alternatives using an aggregated indicator defined by attaching weights to impacts. We address here the inverse problem. That is, given an alternative, we aim to determine the weights for which that solution becomes optimal. Methods We propose a method based on linear programming (LP) that determines, for a given alternative, the ranges within which the weights attached to a set of impact metrics must lie so that when a weighting combination of these impacts is optimized, the alternative can be optimal, while if the weights fall outside this range, it is guaranteed that the solution will be suboptimal. A large weight value implies that the corresponding LCA impact is given more importance, while a low value implies the converse. Furthermore, we provide a rigorous mathematical analysis on the implications of using weighting schemes in LCA, showing that this practice guides decision-making towards the adoption of some specific alternatives (those lying on the convex envelope of the resulting trade-off curve). Results and discussion A case study based on the design of hydrogen infrastructures is taken as a test bed to illustrate the capabilities of the approach presented. Given are a set of production and storage technologies available to produce and deliver hydrogen, a final demand, and cost and environmental data. A set of designs, each achieving a unique combination of cost and LCA impact, is considered. For each of them, we calculate the minimum and maximum weight to be given to every LCA impact so that the alternative can be optimal among all the candidate designs. Numerical results show that solutions with lower impact are selected when decision makers are willing to pay larger monetary penalties for the environmental damage caused. Conclusions LP can be used in LCA to translate the decision makers’ preferences into weights. This information is rather valuable, particularly when these weights represent economic penalties, as it allows screening and ranking alternatives on the basis of a common economic basis. 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on the use of weighting in lca: translating decision makers’ preferences into weights via linear programming |
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On the use of weighting in LCA: translating decision makers’ preferences into weights via linear programming |
| abstract |
Purpose The main goal of any life cycle assessment (LCA) study is to identify solutions leading to environmental savings. In conventional LCA studies, practitioners select from some alternatives the one which better matches their preferences. This task is sometimes simplified by ranking these alternatives using an aggregated indicator defined by attaching weights to impacts. We address here the inverse problem. That is, given an alternative, we aim to determine the weights for which that solution becomes optimal. Methods We propose a method based on linear programming (LP) that determines, for a given alternative, the ranges within which the weights attached to a set of impact metrics must lie so that when a weighting combination of these impacts is optimized, the alternative can be optimal, while if the weights fall outside this range, it is guaranteed that the solution will be suboptimal. A large weight value implies that the corresponding LCA impact is given more importance, while a low value implies the converse. Furthermore, we provide a rigorous mathematical analysis on the implications of using weighting schemes in LCA, showing that this practice guides decision-making towards the adoption of some specific alternatives (those lying on the convex envelope of the resulting trade-off curve). Results and discussion A case study based on the design of hydrogen infrastructures is taken as a test bed to illustrate the capabilities of the approach presented. Given are a set of production and storage technologies available to produce and deliver hydrogen, a final demand, and cost and environmental data. A set of designs, each achieving a unique combination of cost and LCA impact, is considered. For each of them, we calculate the minimum and maximum weight to be given to every LCA impact so that the alternative can be optimal among all the candidate designs. Numerical results show that solutions with lower impact are selected when decision makers are willing to pay larger monetary penalties for the environmental damage caused. Conclusions LP can be used in LCA to translate the decision makers’ preferences into weights. This information is rather valuable, particularly when these weights represent economic penalties, as it allows screening and ranking alternatives on the basis of a common economic basis. Our framework is aimed at facilitating decision making in LCA studies and defines a general framework for comparing alternatives that show different performance in a wide variety of impact metrics. © Springer-Verlag Berlin Heidelberg 2013 |
| abstractGer |
Purpose The main goal of any life cycle assessment (LCA) study is to identify solutions leading to environmental savings. In conventional LCA studies, practitioners select from some alternatives the one which better matches their preferences. This task is sometimes simplified by ranking these alternatives using an aggregated indicator defined by attaching weights to impacts. We address here the inverse problem. That is, given an alternative, we aim to determine the weights for which that solution becomes optimal. Methods We propose a method based on linear programming (LP) that determines, for a given alternative, the ranges within which the weights attached to a set of impact metrics must lie so that when a weighting combination of these impacts is optimized, the alternative can be optimal, while if the weights fall outside this range, it is guaranteed that the solution will be suboptimal. A large weight value implies that the corresponding LCA impact is given more importance, while a low value implies the converse. Furthermore, we provide a rigorous mathematical analysis on the implications of using weighting schemes in LCA, showing that this practice guides decision-making towards the adoption of some specific alternatives (those lying on the convex envelope of the resulting trade-off curve). Results and discussion A case study based on the design of hydrogen infrastructures is taken as a test bed to illustrate the capabilities of the approach presented. Given are a set of production and storage technologies available to produce and deliver hydrogen, a final demand, and cost and environmental data. A set of designs, each achieving a unique combination of cost and LCA impact, is considered. For each of them, we calculate the minimum and maximum weight to be given to every LCA impact so that the alternative can be optimal among all the candidate designs. Numerical results show that solutions with lower impact are selected when decision makers are willing to pay larger monetary penalties for the environmental damage caused. Conclusions LP can be used in LCA to translate the decision makers’ preferences into weights. This information is rather valuable, particularly when these weights represent economic penalties, as it allows screening and ranking alternatives on the basis of a common economic basis. Our framework is aimed at facilitating decision making in LCA studies and defines a general framework for comparing alternatives that show different performance in a wide variety of impact metrics. © Springer-Verlag Berlin Heidelberg 2013 |
| abstract_unstemmed |
Purpose The main goal of any life cycle assessment (LCA) study is to identify solutions leading to environmental savings. In conventional LCA studies, practitioners select from some alternatives the one which better matches their preferences. This task is sometimes simplified by ranking these alternatives using an aggregated indicator defined by attaching weights to impacts. We address here the inverse problem. That is, given an alternative, we aim to determine the weights for which that solution becomes optimal. Methods We propose a method based on linear programming (LP) that determines, for a given alternative, the ranges within which the weights attached to a set of impact metrics must lie so that when a weighting combination of these impacts is optimized, the alternative can be optimal, while if the weights fall outside this range, it is guaranteed that the solution will be suboptimal. A large weight value implies that the corresponding LCA impact is given more importance, while a low value implies the converse. Furthermore, we provide a rigorous mathematical analysis on the implications of using weighting schemes in LCA, showing that this practice guides decision-making towards the adoption of some specific alternatives (those lying on the convex envelope of the resulting trade-off curve). Results and discussion A case study based on the design of hydrogen infrastructures is taken as a test bed to illustrate the capabilities of the approach presented. Given are a set of production and storage technologies available to produce and deliver hydrogen, a final demand, and cost and environmental data. A set of designs, each achieving a unique combination of cost and LCA impact, is considered. For each of them, we calculate the minimum and maximum weight to be given to every LCA impact so that the alternative can be optimal among all the candidate designs. Numerical results show that solutions with lower impact are selected when decision makers are willing to pay larger monetary penalties for the environmental damage caused. Conclusions LP can be used in LCA to translate the decision makers’ preferences into weights. This information is rather valuable, particularly when these weights represent economic penalties, as it allows screening and ranking alternatives on the basis of a common economic basis. Our framework is aimed at facilitating decision making in LCA studies and defines a general framework for comparing alternatives that show different performance in a wide variety of impact metrics. © Springer-Verlag Berlin Heidelberg 2013 |
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