Implementation of a multicriteria analytical framework for the sustainability evaluation and comparison of concrete materials considering methodological uncertainties
Multicriteria analysis (MCA) is one practical tool to support sustainability decision-making processes considering numerous evaluation criteria. Its conventional analytical stages include the selection of indicators, normalization, weighting, and aggregation. The decisions based on conventional MCA...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Opon, Joel [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022transfer abstract |
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| Übergeordnetes Werk: |
Enthalten in: Self-assembled 3D hierarchical MnCO - Rajendiran, Rajmohan ELSEVIER, 2020, Amsterdam [u.a.] |
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| Übergeordnetes Werk: |
volume:344 ; year:2022 ; day:10 ; month:04 ; pages:0 |
| Links: |
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| DOI / URN: |
10.1016/j.jclepro.2022.131057 |
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| 520 | |a Multicriteria analysis (MCA) is one practical tool to support sustainability decision-making processes considering numerous evaluation criteria. Its conventional analytical stages include the selection of indicators, normalization, weighting, and aggregation. The decisions based on conventional MCA stages, however, could be subject to criticism and bias because each analytical stage can be performed in a multiple number of ways, creating methodological uncertainties which ultimately lead to uncertainty in the MCA output. This paper tackles how to address methodological uncertainties objectively through a practical implementation of an MCA framework for sustainability evaluation under uncertainty. The implementation is demonstrated by a concrete material selection problem, wherein the sustainability performance of different ready-mix concretes are compared, and the “most sustainable” alternative is identified. The unique characteristics of the framework are the use of uncertainty and sensitivity analyses, which transform the results to a probabilistic form, and provide quantitative measures for the objective management of methodological uncertainties. Due to the stochastic nature of the result, a probabilistic tool to hierarchically order the concrete mixes according to their sustainability performance is also developed. This facilities the identification of the “most sustainable” alternative concrete mix while also considering the level of uncertainty associated with that result, thus highlighting the applicability of the framework to support sustainable decision-making under methodological uncertainties. | ||
| 520 | |a Multicriteria analysis (MCA) is one practical tool to support sustainability decision-making processes considering numerous evaluation criteria. Its conventional analytical stages include the selection of indicators, normalization, weighting, and aggregation. The decisions based on conventional MCA stages, however, could be subject to criticism and bias because each analytical stage can be performed in a multiple number of ways, creating methodological uncertainties which ultimately lead to uncertainty in the MCA output. This paper tackles how to address methodological uncertainties objectively through a practical implementation of an MCA framework for sustainability evaluation under uncertainty. The implementation is demonstrated by a concrete material selection problem, wherein the sustainability performance of different ready-mix concretes are compared, and the “most sustainable” alternative is identified. The unique characteristics of the framework are the use of uncertainty and sensitivity analyses, which transform the results to a probabilistic form, and provide quantitative measures for the objective management of methodological uncertainties. Due to the stochastic nature of the result, a probabilistic tool to hierarchically order the concrete mixes according to their sustainability performance is also developed. This facilities the identification of the “most sustainable” alternative concrete mix while also considering the level of uncertainty associated with that result, thus highlighting the applicability of the framework to support sustainable decision-making under methodological uncertainties. | ||
| 650 | 7 | |a Sensitivity analysis |2 Elsevier | |
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10.1016/j.jclepro.2022.131057 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001927.pica (DE-627)ELV057134251 (ELSEVIER)S0959-6526(22)00690-4 DE-627 ger DE-627 rakwb eng 540 VZ 35.18 bkl Opon, Joel verfasserin aut Implementation of a multicriteria analytical framework for the sustainability evaluation and comparison of concrete materials considering methodological uncertainties 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Multicriteria analysis (MCA) is one practical tool to support sustainability decision-making processes considering numerous evaluation criteria. Its conventional analytical stages include the selection of indicators, normalization, weighting, and aggregation. The decisions based on conventional MCA stages, however, could be subject to criticism and bias because each analytical stage can be performed in a multiple number of ways, creating methodological uncertainties which ultimately lead to uncertainty in the MCA output. This paper tackles how to address methodological uncertainties objectively through a practical implementation of an MCA framework for sustainability evaluation under uncertainty. The implementation is demonstrated by a concrete material selection problem, wherein the sustainability performance of different ready-mix concretes are compared, and the “most sustainable” alternative is identified. The unique characteristics of the framework are the use of uncertainty and sensitivity analyses, which transform the results to a probabilistic form, and provide quantitative measures for the objective management of methodological uncertainties. Due to the stochastic nature of the result, a probabilistic tool to hierarchically order the concrete mixes according to their sustainability performance is also developed. This facilities the identification of the “most sustainable” alternative concrete mix while also considering the level of uncertainty associated with that result, thus highlighting the applicability of the framework to support sustainable decision-making under methodological uncertainties. Multicriteria analysis (MCA) is one practical tool to support sustainability decision-making processes considering numerous evaluation criteria. Its conventional analytical stages include the selection of indicators, normalization, weighting, and aggregation. The decisions based on conventional MCA stages, however, could be subject to criticism and bias because each analytical stage can be performed in a multiple number of ways, creating methodological uncertainties which ultimately lead to uncertainty in the MCA output. This paper tackles how to address methodological uncertainties objectively through a practical implementation of an MCA framework for sustainability evaluation under uncertainty. The implementation is demonstrated by a concrete material selection problem, wherein the sustainability performance of different ready-mix concretes are compared, and the “most sustainable” alternative is identified. The unique characteristics of the framework are the use of uncertainty and sensitivity analyses, which transform the results to a probabilistic form, and provide quantitative measures for the objective management of methodological uncertainties. Due to the stochastic nature of the result, a probabilistic tool to hierarchically order the concrete mixes according to their sustainability performance is also developed. This facilities the identification of the “most sustainable” alternative concrete mix while also considering the level of uncertainty associated with that result, thus highlighting the applicability of the framework to support sustainable decision-making under methodological uncertainties. Sensitivity analysis Elsevier Multicriteria analysis Elsevier Sustainability evaluation Elsevier Uncertainty analysis Elsevier Sustainable concrete Elsevier Henry, Michael oth Enthalten in Elsevier Science Rajendiran, Rajmohan ELSEVIER Self-assembled 3D hierarchical MnCO 2020 Amsterdam [u.a.] (DE-627)ELV003750353 volume:344 year:2022 day:10 month:04 pages:0 https://doi.org/10.1016/j.jclepro.2022.131057 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 35.18 Kolloidchemie Grenzflächenchemie VZ AR 344 2022 10 0410 0 |
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10.1016/j.jclepro.2022.131057 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001927.pica (DE-627)ELV057134251 (ELSEVIER)S0959-6526(22)00690-4 DE-627 ger DE-627 rakwb eng 540 VZ 35.18 bkl Opon, Joel verfasserin aut Implementation of a multicriteria analytical framework for the sustainability evaluation and comparison of concrete materials considering methodological uncertainties 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Multicriteria analysis (MCA) is one practical tool to support sustainability decision-making processes considering numerous evaluation criteria. Its conventional analytical stages include the selection of indicators, normalization, weighting, and aggregation. The decisions based on conventional MCA stages, however, could be subject to criticism and bias because each analytical stage can be performed in a multiple number of ways, creating methodological uncertainties which ultimately lead to uncertainty in the MCA output. This paper tackles how to address methodological uncertainties objectively through a practical implementation of an MCA framework for sustainability evaluation under uncertainty. The implementation is demonstrated by a concrete material selection problem, wherein the sustainability performance of different ready-mix concretes are compared, and the “most sustainable” alternative is identified. The unique characteristics of the framework are the use of uncertainty and sensitivity analyses, which transform the results to a probabilistic form, and provide quantitative measures for the objective management of methodological uncertainties. Due to the stochastic nature of the result, a probabilistic tool to hierarchically order the concrete mixes according to their sustainability performance is also developed. This facilities the identification of the “most sustainable” alternative concrete mix while also considering the level of uncertainty associated with that result, thus highlighting the applicability of the framework to support sustainable decision-making under methodological uncertainties. Multicriteria analysis (MCA) is one practical tool to support sustainability decision-making processes considering numerous evaluation criteria. Its conventional analytical stages include the selection of indicators, normalization, weighting, and aggregation. The decisions based on conventional MCA stages, however, could be subject to criticism and bias because each analytical stage can be performed in a multiple number of ways, creating methodological uncertainties which ultimately lead to uncertainty in the MCA output. This paper tackles how to address methodological uncertainties objectively through a practical implementation of an MCA framework for sustainability evaluation under uncertainty. The implementation is demonstrated by a concrete material selection problem, wherein the sustainability performance of different ready-mix concretes are compared, and the “most sustainable” alternative is identified. The unique characteristics of the framework are the use of uncertainty and sensitivity analyses, which transform the results to a probabilistic form, and provide quantitative measures for the objective management of methodological uncertainties. Due to the stochastic nature of the result, a probabilistic tool to hierarchically order the concrete mixes according to their sustainability performance is also developed. This facilities the identification of the “most sustainable” alternative concrete mix while also considering the level of uncertainty associated with that result, thus highlighting the applicability of the framework to support sustainable decision-making under methodological uncertainties. Sensitivity analysis Elsevier Multicriteria analysis Elsevier Sustainability evaluation Elsevier Uncertainty analysis Elsevier Sustainable concrete Elsevier Henry, Michael oth Enthalten in Elsevier Science Rajendiran, Rajmohan ELSEVIER Self-assembled 3D hierarchical MnCO 2020 Amsterdam [u.a.] (DE-627)ELV003750353 volume:344 year:2022 day:10 month:04 pages:0 https://doi.org/10.1016/j.jclepro.2022.131057 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 35.18 Kolloidchemie Grenzflächenchemie VZ AR 344 2022 10 0410 0 |
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10.1016/j.jclepro.2022.131057 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001927.pica (DE-627)ELV057134251 (ELSEVIER)S0959-6526(22)00690-4 DE-627 ger DE-627 rakwb eng 540 VZ 35.18 bkl Opon, Joel verfasserin aut Implementation of a multicriteria analytical framework for the sustainability evaluation and comparison of concrete materials considering methodological uncertainties 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Multicriteria analysis (MCA) is one practical tool to support sustainability decision-making processes considering numerous evaluation criteria. Its conventional analytical stages include the selection of indicators, normalization, weighting, and aggregation. The decisions based on conventional MCA stages, however, could be subject to criticism and bias because each analytical stage can be performed in a multiple number of ways, creating methodological uncertainties which ultimately lead to uncertainty in the MCA output. This paper tackles how to address methodological uncertainties objectively through a practical implementation of an MCA framework for sustainability evaluation under uncertainty. The implementation is demonstrated by a concrete material selection problem, wherein the sustainability performance of different ready-mix concretes are compared, and the “most sustainable” alternative is identified. The unique characteristics of the framework are the use of uncertainty and sensitivity analyses, which transform the results to a probabilistic form, and provide quantitative measures for the objective management of methodological uncertainties. Due to the stochastic nature of the result, a probabilistic tool to hierarchically order the concrete mixes according to their sustainability performance is also developed. This facilities the identification of the “most sustainable” alternative concrete mix while also considering the level of uncertainty associated with that result, thus highlighting the applicability of the framework to support sustainable decision-making under methodological uncertainties. Multicriteria analysis (MCA) is one practical tool to support sustainability decision-making processes considering numerous evaluation criteria. Its conventional analytical stages include the selection of indicators, normalization, weighting, and aggregation. The decisions based on conventional MCA stages, however, could be subject to criticism and bias because each analytical stage can be performed in a multiple number of ways, creating methodological uncertainties which ultimately lead to uncertainty in the MCA output. This paper tackles how to address methodological uncertainties objectively through a practical implementation of an MCA framework for sustainability evaluation under uncertainty. The implementation is demonstrated by a concrete material selection problem, wherein the sustainability performance of different ready-mix concretes are compared, and the “most sustainable” alternative is identified. The unique characteristics of the framework are the use of uncertainty and sensitivity analyses, which transform the results to a probabilistic form, and provide quantitative measures for the objective management of methodological uncertainties. Due to the stochastic nature of the result, a probabilistic tool to hierarchically order the concrete mixes according to their sustainability performance is also developed. This facilities the identification of the “most sustainable” alternative concrete mix while also considering the level of uncertainty associated with that result, thus highlighting the applicability of the framework to support sustainable decision-making under methodological uncertainties. Sensitivity analysis Elsevier Multicriteria analysis Elsevier Sustainability evaluation Elsevier Uncertainty analysis Elsevier Sustainable concrete Elsevier Henry, Michael oth Enthalten in Elsevier Science Rajendiran, Rajmohan ELSEVIER Self-assembled 3D hierarchical MnCO 2020 Amsterdam [u.a.] (DE-627)ELV003750353 volume:344 year:2022 day:10 month:04 pages:0 https://doi.org/10.1016/j.jclepro.2022.131057 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 35.18 Kolloidchemie Grenzflächenchemie VZ AR 344 2022 10 0410 0 |
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10.1016/j.jclepro.2022.131057 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001927.pica (DE-627)ELV057134251 (ELSEVIER)S0959-6526(22)00690-4 DE-627 ger DE-627 rakwb eng 540 VZ 35.18 bkl Opon, Joel verfasserin aut Implementation of a multicriteria analytical framework for the sustainability evaluation and comparison of concrete materials considering methodological uncertainties 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Multicriteria analysis (MCA) is one practical tool to support sustainability decision-making processes considering numerous evaluation criteria. Its conventional analytical stages include the selection of indicators, normalization, weighting, and aggregation. The decisions based on conventional MCA stages, however, could be subject to criticism and bias because each analytical stage can be performed in a multiple number of ways, creating methodological uncertainties which ultimately lead to uncertainty in the MCA output. This paper tackles how to address methodological uncertainties objectively through a practical implementation of an MCA framework for sustainability evaluation under uncertainty. The implementation is demonstrated by a concrete material selection problem, wherein the sustainability performance of different ready-mix concretes are compared, and the “most sustainable” alternative is identified. The unique characteristics of the framework are the use of uncertainty and sensitivity analyses, which transform the results to a probabilistic form, and provide quantitative measures for the objective management of methodological uncertainties. Due to the stochastic nature of the result, a probabilistic tool to hierarchically order the concrete mixes according to their sustainability performance is also developed. This facilities the identification of the “most sustainable” alternative concrete mix while also considering the level of uncertainty associated with that result, thus highlighting the applicability of the framework to support sustainable decision-making under methodological uncertainties. Multicriteria analysis (MCA) is one practical tool to support sustainability decision-making processes considering numerous evaluation criteria. Its conventional analytical stages include the selection of indicators, normalization, weighting, and aggregation. The decisions based on conventional MCA stages, however, could be subject to criticism and bias because each analytical stage can be performed in a multiple number of ways, creating methodological uncertainties which ultimately lead to uncertainty in the MCA output. This paper tackles how to address methodological uncertainties objectively through a practical implementation of an MCA framework for sustainability evaluation under uncertainty. The implementation is demonstrated by a concrete material selection problem, wherein the sustainability performance of different ready-mix concretes are compared, and the “most sustainable” alternative is identified. The unique characteristics of the framework are the use of uncertainty and sensitivity analyses, which transform the results to a probabilistic form, and provide quantitative measures for the objective management of methodological uncertainties. Due to the stochastic nature of the result, a probabilistic tool to hierarchically order the concrete mixes according to their sustainability performance is also developed. This facilities the identification of the “most sustainable” alternative concrete mix while also considering the level of uncertainty associated with that result, thus highlighting the applicability of the framework to support sustainable decision-making under methodological uncertainties. Sensitivity analysis Elsevier Multicriteria analysis Elsevier Sustainability evaluation Elsevier Uncertainty analysis Elsevier Sustainable concrete Elsevier Henry, Michael oth Enthalten in Elsevier Science Rajendiran, Rajmohan ELSEVIER Self-assembled 3D hierarchical MnCO 2020 Amsterdam [u.a.] (DE-627)ELV003750353 volume:344 year:2022 day:10 month:04 pages:0 https://doi.org/10.1016/j.jclepro.2022.131057 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 35.18 Kolloidchemie Grenzflächenchemie VZ AR 344 2022 10 0410 0 |
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10.1016/j.jclepro.2022.131057 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001927.pica (DE-627)ELV057134251 (ELSEVIER)S0959-6526(22)00690-4 DE-627 ger DE-627 rakwb eng 540 VZ 35.18 bkl Opon, Joel verfasserin aut Implementation of a multicriteria analytical framework for the sustainability evaluation and comparison of concrete materials considering methodological uncertainties 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Multicriteria analysis (MCA) is one practical tool to support sustainability decision-making processes considering numerous evaluation criteria. Its conventional analytical stages include the selection of indicators, normalization, weighting, and aggregation. The decisions based on conventional MCA stages, however, could be subject to criticism and bias because each analytical stage can be performed in a multiple number of ways, creating methodological uncertainties which ultimately lead to uncertainty in the MCA output. This paper tackles how to address methodological uncertainties objectively through a practical implementation of an MCA framework for sustainability evaluation under uncertainty. The implementation is demonstrated by a concrete material selection problem, wherein the sustainability performance of different ready-mix concretes are compared, and the “most sustainable” alternative is identified. The unique characteristics of the framework are the use of uncertainty and sensitivity analyses, which transform the results to a probabilistic form, and provide quantitative measures for the objective management of methodological uncertainties. Due to the stochastic nature of the result, a probabilistic tool to hierarchically order the concrete mixes according to their sustainability performance is also developed. This facilities the identification of the “most sustainable” alternative concrete mix while also considering the level of uncertainty associated with that result, thus highlighting the applicability of the framework to support sustainable decision-making under methodological uncertainties. Multicriteria analysis (MCA) is one practical tool to support sustainability decision-making processes considering numerous evaluation criteria. Its conventional analytical stages include the selection of indicators, normalization, weighting, and aggregation. The decisions based on conventional MCA stages, however, could be subject to criticism and bias because each analytical stage can be performed in a multiple number of ways, creating methodological uncertainties which ultimately lead to uncertainty in the MCA output. This paper tackles how to address methodological uncertainties objectively through a practical implementation of an MCA framework for sustainability evaluation under uncertainty. The implementation is demonstrated by a concrete material selection problem, wherein the sustainability performance of different ready-mix concretes are compared, and the “most sustainable” alternative is identified. The unique characteristics of the framework are the use of uncertainty and sensitivity analyses, which transform the results to a probabilistic form, and provide quantitative measures for the objective management of methodological uncertainties. Due to the stochastic nature of the result, a probabilistic tool to hierarchically order the concrete mixes according to their sustainability performance is also developed. This facilities the identification of the “most sustainable” alternative concrete mix while also considering the level of uncertainty associated with that result, thus highlighting the applicability of the framework to support sustainable decision-making under methodological uncertainties. Sensitivity analysis Elsevier Multicriteria analysis Elsevier Sustainability evaluation Elsevier Uncertainty analysis Elsevier Sustainable concrete Elsevier Henry, Michael oth Enthalten in Elsevier Science Rajendiran, Rajmohan ELSEVIER Self-assembled 3D hierarchical MnCO 2020 Amsterdam [u.a.] (DE-627)ELV003750353 volume:344 year:2022 day:10 month:04 pages:0 https://doi.org/10.1016/j.jclepro.2022.131057 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 35.18 Kolloidchemie Grenzflächenchemie VZ AR 344 2022 10 0410 0 |
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Implementation of a multicriteria analytical framework for the sustainability evaluation and comparison of concrete materials considering methodological uncertainties |
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Multicriteria analysis (MCA) is one practical tool to support sustainability decision-making processes considering numerous evaluation criteria. Its conventional analytical stages include the selection of indicators, normalization, weighting, and aggregation. The decisions based on conventional MCA stages, however, could be subject to criticism and bias because each analytical stage can be performed in a multiple number of ways, creating methodological uncertainties which ultimately lead to uncertainty in the MCA output. This paper tackles how to address methodological uncertainties objectively through a practical implementation of an MCA framework for sustainability evaluation under uncertainty. The implementation is demonstrated by a concrete material selection problem, wherein the sustainability performance of different ready-mix concretes are compared, and the “most sustainable” alternative is identified. The unique characteristics of the framework are the use of uncertainty and sensitivity analyses, which transform the results to a probabilistic form, and provide quantitative measures for the objective management of methodological uncertainties. Due to the stochastic nature of the result, a probabilistic tool to hierarchically order the concrete mixes according to their sustainability performance is also developed. This facilities the identification of the “most sustainable” alternative concrete mix while also considering the level of uncertainty associated with that result, thus highlighting the applicability of the framework to support sustainable decision-making under methodological uncertainties. |
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Multicriteria analysis (MCA) is one practical tool to support sustainability decision-making processes considering numerous evaluation criteria. Its conventional analytical stages include the selection of indicators, normalization, weighting, and aggregation. The decisions based on conventional MCA stages, however, could be subject to criticism and bias because each analytical stage can be performed in a multiple number of ways, creating methodological uncertainties which ultimately lead to uncertainty in the MCA output. This paper tackles how to address methodological uncertainties objectively through a practical implementation of an MCA framework for sustainability evaluation under uncertainty. The implementation is demonstrated by a concrete material selection problem, wherein the sustainability performance of different ready-mix concretes are compared, and the “most sustainable” alternative is identified. The unique characteristics of the framework are the use of uncertainty and sensitivity analyses, which transform the results to a probabilistic form, and provide quantitative measures for the objective management of methodological uncertainties. Due to the stochastic nature of the result, a probabilistic tool to hierarchically order the concrete mixes according to their sustainability performance is also developed. This facilities the identification of the “most sustainable” alternative concrete mix while also considering the level of uncertainty associated with that result, thus highlighting the applicability of the framework to support sustainable decision-making under methodological uncertainties. |
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Multicriteria analysis (MCA) is one practical tool to support sustainability decision-making processes considering numerous evaluation criteria. Its conventional analytical stages include the selection of indicators, normalization, weighting, and aggregation. The decisions based on conventional MCA stages, however, could be subject to criticism and bias because each analytical stage can be performed in a multiple number of ways, creating methodological uncertainties which ultimately lead to uncertainty in the MCA output. This paper tackles how to address methodological uncertainties objectively through a practical implementation of an MCA framework for sustainability evaluation under uncertainty. The implementation is demonstrated by a concrete material selection problem, wherein the sustainability performance of different ready-mix concretes are compared, and the “most sustainable” alternative is identified. The unique characteristics of the framework are the use of uncertainty and sensitivity analyses, which transform the results to a probabilistic form, and provide quantitative measures for the objective management of methodological uncertainties. Due to the stochastic nature of the result, a probabilistic tool to hierarchically order the concrete mixes according to their sustainability performance is also developed. This facilities the identification of the “most sustainable” alternative concrete mix while also considering the level of uncertainty associated with that result, thus highlighting the applicability of the framework to support sustainable decision-making under methodological uncertainties. |
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