Hotspot identification in the clothing industry using social life cycle assessment—opportunities and challenges of input-output modelling
Purpose A cradle-to-gate, input/output-based social life cycle assessment (SLCA) was conducted using the Swedish clothing consumption as a case study. The aim was to investigate the influence of the cut-off rule and the definition of “hotspots” in social hotspot assessment. A second aim was to ident...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Zamani, Bahareh [verfasserIn] Sandin, Gustav [verfasserIn] Svanström, Magdalena [verfasserIn] Peters, Greg M. [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2016 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: The international journal of life cycle assessment - Berlin : Springer, 1996, 23(2016), 3 vom: 18. Apr., Seite 536-546 |
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| Übergeordnetes Werk: |
volume:23 ; year:2016 ; number:3 ; day:18 ; month:04 ; pages:536-546 |
| Links: |
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| DOI / URN: |
10.1007/s11367-016-1113-x |
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SPR018939937 |
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| 520 | |a Purpose A cradle-to-gate, input/output-based social life cycle assessment (SLCA) was conducted using the Swedish clothing consumption as a case study. The aim was to investigate the influence of the cut-off rule and the definition of “hotspots” in social hotspot assessment. A second aim was to identify social hotspots of Swedish clothing on a national level. Methods The case study was based on the SLCA methodology provided in the Guidelines for Social Life Cycle Assessment of Products (Benoît and Mazijn 2009). An input/output model was used to define the product system from cradle to gate. The negative social hotspots were evaluated for a set of social indicators that were selected by consumers. The impact assessment was conducted on a sector and country level by using the Social Hotspots Database. The identified sectors of the economy with high and very high levels of risk were listed for each social indicator. Results and discussion The results pinpointed some hotspots throughout the supply chain for Swedish clothing consumption. Some unexpected sectors such as commerce and business services in Bangladesh were identified as important hotspots as well as main sectors in the production phase such as plant fibres, textiles and garments that would be expected also on the bases of a traditional process analysis. A sensitivity analysis on different cut-off values showed the extent to which the choice of cut-off rule can directly affect the results via influence over the number of country-specific sectors (CSSs) in the product system. The influence of the hotspot definition was investigated by evaluating the working hour intensity for low- and medium-risk levels for three different indicators. The results show that for child labour, 92 % of the share of working hours was associated with low- and medium-risk levels. Therefore, the evaluation of risk levels other than high and very high can provide a more complete picture of the hotspots. Conclusions The application of input/output-based SLCA on the clothing production supply chain provided a more complete picture of the social hotspots than with traditional process-based SLCA. Some unexpected sectors related to commerce and business appeared as social hotspots in the clothing industry. The study explored some important parameters in applying an input/output-based SLCA. The results show that the cut-off values and definition of hotspots in relation to risk levels can directly influence the results. | ||
| 650 | 4 | |a Fashion |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Social Hotspot identification |7 (dpeaa)DE-He213 | |
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| 650 | 4 | |a Social impact |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Sandin, Gustav |e verfasserin |4 aut | |
| 700 | 1 | |a Svanström, Magdalena |e verfasserin |4 aut | |
| 700 | 1 | |a Peters, Greg M. |e verfasserin |4 aut | |
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10.1007/s11367-016-1113-x doi (DE-627)SPR018939937 (SPR)s11367-016-1113-x-e DE-627 ger DE-627 rakwb eng 690 ASE 43.33 bkl 85.15 bkl 85.35 bkl Zamani, Bahareh verfasserin aut Hotspot identification in the clothing industry using social life cycle assessment—opportunities and challenges of input-output modelling 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Purpose A cradle-to-gate, input/output-based social life cycle assessment (SLCA) was conducted using the Swedish clothing consumption as a case study. The aim was to investigate the influence of the cut-off rule and the definition of “hotspots” in social hotspot assessment. A second aim was to identify social hotspots of Swedish clothing on a national level. Methods The case study was based on the SLCA methodology provided in the Guidelines for Social Life Cycle Assessment of Products (Benoît and Mazijn 2009). An input/output model was used to define the product system from cradle to gate. The negative social hotspots were evaluated for a set of social indicators that were selected by consumers. The impact assessment was conducted on a sector and country level by using the Social Hotspots Database. The identified sectors of the economy with high and very high levels of risk were listed for each social indicator. Results and discussion The results pinpointed some hotspots throughout the supply chain for Swedish clothing consumption. Some unexpected sectors such as commerce and business services in Bangladesh were identified as important hotspots as well as main sectors in the production phase such as plant fibres, textiles and garments that would be expected also on the bases of a traditional process analysis. A sensitivity analysis on different cut-off values showed the extent to which the choice of cut-off rule can directly affect the results via influence over the number of country-specific sectors (CSSs) in the product system. The influence of the hotspot definition was investigated by evaluating the working hour intensity for low- and medium-risk levels for three different indicators. The results show that for child labour, 92 % of the share of working hours was associated with low- and medium-risk levels. Therefore, the evaluation of risk levels other than high and very high can provide a more complete picture of the hotspots. Conclusions The application of input/output-based SLCA on the clothing production supply chain provided a more complete picture of the social hotspots than with traditional process-based SLCA. Some unexpected sectors related to commerce and business appeared as social hotspots in the clothing industry. The study explored some important parameters in applying an input/output-based SLCA. The results show that the cut-off values and definition of hotspots in relation to risk levels can directly influence the results. Fashion (dpeaa)DE-He213 Social Hotspot identification (dpeaa)DE-He213 SHDB (dpeaa)DE-He213 SLCA (dpeaa)DE-He213 Social Hotspots Database (dpeaa)DE-He213 Social impact (dpeaa)DE-He213 Sandin, Gustav verfasserin aut Svanström, Magdalena verfasserin aut Peters, Greg M. verfasserin aut Enthalten in The international journal of life cycle assessment Berlin : Springer, 1996 23(2016), 3 vom: 18. Apr., Seite 536-546 (DE-627)313652961 (DE-600)2009386-X 1614-7502 nnns volume:23 year:2016 number:3 day:18 month:04 pages:536-546 https://dx.doi.org/10.1007/s11367-016-1113-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GGO SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 43.33 ASE 85.15 ASE 85.35 ASE AR 23 2016 3 18 04 536-546 |
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10.1007/s11367-016-1113-x doi (DE-627)SPR018939937 (SPR)s11367-016-1113-x-e DE-627 ger DE-627 rakwb eng 690 ASE 43.33 bkl 85.15 bkl 85.35 bkl Zamani, Bahareh verfasserin aut Hotspot identification in the clothing industry using social life cycle assessment—opportunities and challenges of input-output modelling 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Purpose A cradle-to-gate, input/output-based social life cycle assessment (SLCA) was conducted using the Swedish clothing consumption as a case study. The aim was to investigate the influence of the cut-off rule and the definition of “hotspots” in social hotspot assessment. A second aim was to identify social hotspots of Swedish clothing on a national level. Methods The case study was based on the SLCA methodology provided in the Guidelines for Social Life Cycle Assessment of Products (Benoît and Mazijn 2009). An input/output model was used to define the product system from cradle to gate. The negative social hotspots were evaluated for a set of social indicators that were selected by consumers. The impact assessment was conducted on a sector and country level by using the Social Hotspots Database. The identified sectors of the economy with high and very high levels of risk were listed for each social indicator. Results and discussion The results pinpointed some hotspots throughout the supply chain for Swedish clothing consumption. Some unexpected sectors such as commerce and business services in Bangladesh were identified as important hotspots as well as main sectors in the production phase such as plant fibres, textiles and garments that would be expected also on the bases of a traditional process analysis. A sensitivity analysis on different cut-off values showed the extent to which the choice of cut-off rule can directly affect the results via influence over the number of country-specific sectors (CSSs) in the product system. The influence of the hotspot definition was investigated by evaluating the working hour intensity for low- and medium-risk levels for three different indicators. The results show that for child labour, 92 % of the share of working hours was associated with low- and medium-risk levels. Therefore, the evaluation of risk levels other than high and very high can provide a more complete picture of the hotspots. Conclusions The application of input/output-based SLCA on the clothing production supply chain provided a more complete picture of the social hotspots than with traditional process-based SLCA. Some unexpected sectors related to commerce and business appeared as social hotspots in the clothing industry. The study explored some important parameters in applying an input/output-based SLCA. The results show that the cut-off values and definition of hotspots in relation to risk levels can directly influence the results. Fashion (dpeaa)DE-He213 Social Hotspot identification (dpeaa)DE-He213 SHDB (dpeaa)DE-He213 SLCA (dpeaa)DE-He213 Social Hotspots Database (dpeaa)DE-He213 Social impact (dpeaa)DE-He213 Sandin, Gustav verfasserin aut Svanström, Magdalena verfasserin aut Peters, Greg M. verfasserin aut Enthalten in The international journal of life cycle assessment Berlin : Springer, 1996 23(2016), 3 vom: 18. Apr., Seite 536-546 (DE-627)313652961 (DE-600)2009386-X 1614-7502 nnns volume:23 year:2016 number:3 day:18 month:04 pages:536-546 https://dx.doi.org/10.1007/s11367-016-1113-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GGO SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 43.33 ASE 85.15 ASE 85.35 ASE AR 23 2016 3 18 04 536-546 |
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10.1007/s11367-016-1113-x doi (DE-627)SPR018939937 (SPR)s11367-016-1113-x-e DE-627 ger DE-627 rakwb eng 690 ASE 43.33 bkl 85.15 bkl 85.35 bkl Zamani, Bahareh verfasserin aut Hotspot identification in the clothing industry using social life cycle assessment—opportunities and challenges of input-output modelling 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Purpose A cradle-to-gate, input/output-based social life cycle assessment (SLCA) was conducted using the Swedish clothing consumption as a case study. The aim was to investigate the influence of the cut-off rule and the definition of “hotspots” in social hotspot assessment. A second aim was to identify social hotspots of Swedish clothing on a national level. Methods The case study was based on the SLCA methodology provided in the Guidelines for Social Life Cycle Assessment of Products (Benoît and Mazijn 2009). An input/output model was used to define the product system from cradle to gate. The negative social hotspots were evaluated for a set of social indicators that were selected by consumers. The impact assessment was conducted on a sector and country level by using the Social Hotspots Database. The identified sectors of the economy with high and very high levels of risk were listed for each social indicator. Results and discussion The results pinpointed some hotspots throughout the supply chain for Swedish clothing consumption. Some unexpected sectors such as commerce and business services in Bangladesh were identified as important hotspots as well as main sectors in the production phase such as plant fibres, textiles and garments that would be expected also on the bases of a traditional process analysis. A sensitivity analysis on different cut-off values showed the extent to which the choice of cut-off rule can directly affect the results via influence over the number of country-specific sectors (CSSs) in the product system. The influence of the hotspot definition was investigated by evaluating the working hour intensity for low- and medium-risk levels for three different indicators. The results show that for child labour, 92 % of the share of working hours was associated with low- and medium-risk levels. Therefore, the evaluation of risk levels other than high and very high can provide a more complete picture of the hotspots. Conclusions The application of input/output-based SLCA on the clothing production supply chain provided a more complete picture of the social hotspots than with traditional process-based SLCA. Some unexpected sectors related to commerce and business appeared as social hotspots in the clothing industry. The study explored some important parameters in applying an input/output-based SLCA. The results show that the cut-off values and definition of hotspots in relation to risk levels can directly influence the results. Fashion (dpeaa)DE-He213 Social Hotspot identification (dpeaa)DE-He213 SHDB (dpeaa)DE-He213 SLCA (dpeaa)DE-He213 Social Hotspots Database (dpeaa)DE-He213 Social impact (dpeaa)DE-He213 Sandin, Gustav verfasserin aut Svanström, Magdalena verfasserin aut Peters, Greg M. verfasserin aut Enthalten in The international journal of life cycle assessment Berlin : Springer, 1996 23(2016), 3 vom: 18. Apr., Seite 536-546 (DE-627)313652961 (DE-600)2009386-X 1614-7502 nnns volume:23 year:2016 number:3 day:18 month:04 pages:536-546 https://dx.doi.org/10.1007/s11367-016-1113-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GGO SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 43.33 ASE 85.15 ASE 85.35 ASE AR 23 2016 3 18 04 536-546 |
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10.1007/s11367-016-1113-x doi (DE-627)SPR018939937 (SPR)s11367-016-1113-x-e DE-627 ger DE-627 rakwb eng 690 ASE 43.33 bkl 85.15 bkl 85.35 bkl Zamani, Bahareh verfasserin aut Hotspot identification in the clothing industry using social life cycle assessment—opportunities and challenges of input-output modelling 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Purpose A cradle-to-gate, input/output-based social life cycle assessment (SLCA) was conducted using the Swedish clothing consumption as a case study. The aim was to investigate the influence of the cut-off rule and the definition of “hotspots” in social hotspot assessment. A second aim was to identify social hotspots of Swedish clothing on a national level. Methods The case study was based on the SLCA methodology provided in the Guidelines for Social Life Cycle Assessment of Products (Benoît and Mazijn 2009). An input/output model was used to define the product system from cradle to gate. The negative social hotspots were evaluated for a set of social indicators that were selected by consumers. The impact assessment was conducted on a sector and country level by using the Social Hotspots Database. The identified sectors of the economy with high and very high levels of risk were listed for each social indicator. Results and discussion The results pinpointed some hotspots throughout the supply chain for Swedish clothing consumption. Some unexpected sectors such as commerce and business services in Bangladesh were identified as important hotspots as well as main sectors in the production phase such as plant fibres, textiles and garments that would be expected also on the bases of a traditional process analysis. A sensitivity analysis on different cut-off values showed the extent to which the choice of cut-off rule can directly affect the results via influence over the number of country-specific sectors (CSSs) in the product system. The influence of the hotspot definition was investigated by evaluating the working hour intensity for low- and medium-risk levels for three different indicators. The results show that for child labour, 92 % of the share of working hours was associated with low- and medium-risk levels. Therefore, the evaluation of risk levels other than high and very high can provide a more complete picture of the hotspots. Conclusions The application of input/output-based SLCA on the clothing production supply chain provided a more complete picture of the social hotspots than with traditional process-based SLCA. Some unexpected sectors related to commerce and business appeared as social hotspots in the clothing industry. The study explored some important parameters in applying an input/output-based SLCA. The results show that the cut-off values and definition of hotspots in relation to risk levels can directly influence the results. Fashion (dpeaa)DE-He213 Social Hotspot identification (dpeaa)DE-He213 SHDB (dpeaa)DE-He213 SLCA (dpeaa)DE-He213 Social Hotspots Database (dpeaa)DE-He213 Social impact (dpeaa)DE-He213 Sandin, Gustav verfasserin aut Svanström, Magdalena verfasserin aut Peters, Greg M. verfasserin aut Enthalten in The international journal of life cycle assessment Berlin : Springer, 1996 23(2016), 3 vom: 18. Apr., Seite 536-546 (DE-627)313652961 (DE-600)2009386-X 1614-7502 nnns volume:23 year:2016 number:3 day:18 month:04 pages:536-546 https://dx.doi.org/10.1007/s11367-016-1113-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GGO SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 43.33 ASE 85.15 ASE 85.35 ASE AR 23 2016 3 18 04 536-546 |
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10.1007/s11367-016-1113-x doi (DE-627)SPR018939937 (SPR)s11367-016-1113-x-e DE-627 ger DE-627 rakwb eng 690 ASE 43.33 bkl 85.15 bkl 85.35 bkl Zamani, Bahareh verfasserin aut Hotspot identification in the clothing industry using social life cycle assessment—opportunities and challenges of input-output modelling 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Purpose A cradle-to-gate, input/output-based social life cycle assessment (SLCA) was conducted using the Swedish clothing consumption as a case study. The aim was to investigate the influence of the cut-off rule and the definition of “hotspots” in social hotspot assessment. A second aim was to identify social hotspots of Swedish clothing on a national level. Methods The case study was based on the SLCA methodology provided in the Guidelines for Social Life Cycle Assessment of Products (Benoît and Mazijn 2009). An input/output model was used to define the product system from cradle to gate. The negative social hotspots were evaluated for a set of social indicators that were selected by consumers. The impact assessment was conducted on a sector and country level by using the Social Hotspots Database. The identified sectors of the economy with high and very high levels of risk were listed for each social indicator. Results and discussion The results pinpointed some hotspots throughout the supply chain for Swedish clothing consumption. Some unexpected sectors such as commerce and business services in Bangladesh were identified as important hotspots as well as main sectors in the production phase such as plant fibres, textiles and garments that would be expected also on the bases of a traditional process analysis. A sensitivity analysis on different cut-off values showed the extent to which the choice of cut-off rule can directly affect the results via influence over the number of country-specific sectors (CSSs) in the product system. The influence of the hotspot definition was investigated by evaluating the working hour intensity for low- and medium-risk levels for three different indicators. The results show that for child labour, 92 % of the share of working hours was associated with low- and medium-risk levels. Therefore, the evaluation of risk levels other than high and very high can provide a more complete picture of the hotspots. Conclusions The application of input/output-based SLCA on the clothing production supply chain provided a more complete picture of the social hotspots than with traditional process-based SLCA. Some unexpected sectors related to commerce and business appeared as social hotspots in the clothing industry. The study explored some important parameters in applying an input/output-based SLCA. The results show that the cut-off values and definition of hotspots in relation to risk levels can directly influence the results. Fashion (dpeaa)DE-He213 Social Hotspot identification (dpeaa)DE-He213 SHDB (dpeaa)DE-He213 SLCA (dpeaa)DE-He213 Social Hotspots Database (dpeaa)DE-He213 Social impact (dpeaa)DE-He213 Sandin, Gustav verfasserin aut Svanström, Magdalena verfasserin aut Peters, Greg M. verfasserin aut Enthalten in The international journal of life cycle assessment Berlin : Springer, 1996 23(2016), 3 vom: 18. Apr., Seite 536-546 (DE-627)313652961 (DE-600)2009386-X 1614-7502 nnns volume:23 year:2016 number:3 day:18 month:04 pages:536-546 https://dx.doi.org/10.1007/s11367-016-1113-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GGO SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 43.33 ASE 85.15 ASE 85.35 ASE AR 23 2016 3 18 04 536-546 |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR018939937</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20220111064014.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">201006s2016 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s11367-016-1113-x</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR018939937</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s11367-016-1113-x-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">690</subfield><subfield code="q">ASE</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">43.33</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">85.15</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">85.35</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Zamani, Bahareh</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Hotspot identification in the clothing industry using social life cycle assessment—opportunities and challenges of input-output modelling</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2016</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Purpose A cradle-to-gate, input/output-based social life cycle assessment (SLCA) was conducted using the Swedish clothing consumption as a case study. The aim was to investigate the influence of the cut-off rule and the definition of “hotspots” in social hotspot assessment. A second aim was to identify social hotspots of Swedish clothing on a national level. Methods The case study was based on the SLCA methodology provided in the Guidelines for Social Life Cycle Assessment of Products (Benoît and Mazijn 2009). An input/output model was used to define the product system from cradle to gate. The negative social hotspots were evaluated for a set of social indicators that were selected by consumers. The impact assessment was conducted on a sector and country level by using the Social Hotspots Database. The identified sectors of the economy with high and very high levels of risk were listed for each social indicator. Results and discussion The results pinpointed some hotspots throughout the supply chain for Swedish clothing consumption. Some unexpected sectors such as commerce and business services in Bangladesh were identified as important hotspots as well as main sectors in the production phase such as plant fibres, textiles and garments that would be expected also on the bases of a traditional process analysis. A sensitivity analysis on different cut-off values showed the extent to which the choice of cut-off rule can directly affect the results via influence over the number of country-specific sectors (CSSs) in the product system. The influence of the hotspot definition was investigated by evaluating the working hour intensity for low- and medium-risk levels for three different indicators. The results show that for child labour, 92 % of the share of working hours was associated with low- and medium-risk levels. Therefore, the evaluation of risk levels other than high and very high can provide a more complete picture of the hotspots. Conclusions The application of input/output-based SLCA on the clothing production supply chain provided a more complete picture of the social hotspots than with traditional process-based SLCA. Some unexpected sectors related to commerce and business appeared as social hotspots in the clothing industry. The study explored some important parameters in applying an input/output-based SLCA. The results show that the cut-off values and definition of hotspots in relation to risk levels can directly influence the results.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Fashion</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Social Hotspot identification</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">SHDB</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">SLCA</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Social Hotspots Database</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Social impact</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Sandin, Gustav</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Svanström, Magdalena</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Peters, Greg M.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">The international journal of life cycle assessment</subfield><subfield code="d">Berlin : Springer, 1996</subfield><subfield code="g">23(2016), 3 vom: 18. Apr., Seite 536-546</subfield><subfield code="w">(DE-627)313652961</subfield><subfield code="w">(DE-600)2009386-X</subfield><subfield code="x">1614-7502</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:23</subfield><subfield code="g">year:2016</subfield><subfield code="g">number:3</subfield><subfield code="g">day:18</subfield><subfield code="g">month:04</subfield><subfield code="g">pages:536-546</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://dx.doi.org/10.1007/s11367-016-1113-x</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_SPRINGER</subfield></datafield><datafield tag="912" 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Zamani, Bahareh |
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Zamani, Bahareh ddc 690 bkl 43.33 bkl 85.15 bkl 85.35 misc Fashion misc Social Hotspot identification misc SHDB misc SLCA misc Social Hotspots Database misc Social impact Hotspot identification in the clothing industry using social life cycle assessment—opportunities and challenges of input-output modelling |
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690 ASE 43.33 bkl 85.15 bkl 85.35 bkl Hotspot identification in the clothing industry using social life cycle assessment—opportunities and challenges of input-output modelling Fashion (dpeaa)DE-He213 Social Hotspot identification (dpeaa)DE-He213 SHDB (dpeaa)DE-He213 SLCA (dpeaa)DE-He213 Social Hotspots Database (dpeaa)DE-He213 Social impact (dpeaa)DE-He213 |
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ddc 690 bkl 43.33 bkl 85.15 bkl 85.35 misc Fashion misc Social Hotspot identification misc SHDB misc SLCA misc Social Hotspots Database misc Social impact |
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Hotspot identification in the clothing industry using social life cycle assessment—opportunities and challenges of input-output modelling |
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Zamani, Bahareh |
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The international journal of life cycle assessment |
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Zamani, Bahareh Sandin, Gustav Svanström, Magdalena Peters, Greg M. |
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Elektronische Aufsätze |
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Zamani, Bahareh |
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hotspot identification in the clothing industry using social life cycle assessment—opportunities and challenges of input-output modelling |
| title_auth |
Hotspot identification in the clothing industry using social life cycle assessment—opportunities and challenges of input-output modelling |
| abstract |
Purpose A cradle-to-gate, input/output-based social life cycle assessment (SLCA) was conducted using the Swedish clothing consumption as a case study. The aim was to investigate the influence of the cut-off rule and the definition of “hotspots” in social hotspot assessment. A second aim was to identify social hotspots of Swedish clothing on a national level. Methods The case study was based on the SLCA methodology provided in the Guidelines for Social Life Cycle Assessment of Products (Benoît and Mazijn 2009). An input/output model was used to define the product system from cradle to gate. The negative social hotspots were evaluated for a set of social indicators that were selected by consumers. The impact assessment was conducted on a sector and country level by using the Social Hotspots Database. The identified sectors of the economy with high and very high levels of risk were listed for each social indicator. Results and discussion The results pinpointed some hotspots throughout the supply chain for Swedish clothing consumption. Some unexpected sectors such as commerce and business services in Bangladesh were identified as important hotspots as well as main sectors in the production phase such as plant fibres, textiles and garments that would be expected also on the bases of a traditional process analysis. A sensitivity analysis on different cut-off values showed the extent to which the choice of cut-off rule can directly affect the results via influence over the number of country-specific sectors (CSSs) in the product system. The influence of the hotspot definition was investigated by evaluating the working hour intensity for low- and medium-risk levels for three different indicators. The results show that for child labour, 92 % of the share of working hours was associated with low- and medium-risk levels. Therefore, the evaluation of risk levels other than high and very high can provide a more complete picture of the hotspots. Conclusions The application of input/output-based SLCA on the clothing production supply chain provided a more complete picture of the social hotspots than with traditional process-based SLCA. Some unexpected sectors related to commerce and business appeared as social hotspots in the clothing industry. The study explored some important parameters in applying an input/output-based SLCA. The results show that the cut-off values and definition of hotspots in relation to risk levels can directly influence the results. |
| abstractGer |
Purpose A cradle-to-gate, input/output-based social life cycle assessment (SLCA) was conducted using the Swedish clothing consumption as a case study. The aim was to investigate the influence of the cut-off rule and the definition of “hotspots” in social hotspot assessment. A second aim was to identify social hotspots of Swedish clothing on a national level. Methods The case study was based on the SLCA methodology provided in the Guidelines for Social Life Cycle Assessment of Products (Benoît and Mazijn 2009). An input/output model was used to define the product system from cradle to gate. The negative social hotspots were evaluated for a set of social indicators that were selected by consumers. The impact assessment was conducted on a sector and country level by using the Social Hotspots Database. The identified sectors of the economy with high and very high levels of risk were listed for each social indicator. Results and discussion The results pinpointed some hotspots throughout the supply chain for Swedish clothing consumption. Some unexpected sectors such as commerce and business services in Bangladesh were identified as important hotspots as well as main sectors in the production phase such as plant fibres, textiles and garments that would be expected also on the bases of a traditional process analysis. A sensitivity analysis on different cut-off values showed the extent to which the choice of cut-off rule can directly affect the results via influence over the number of country-specific sectors (CSSs) in the product system. The influence of the hotspot definition was investigated by evaluating the working hour intensity for low- and medium-risk levels for three different indicators. The results show that for child labour, 92 % of the share of working hours was associated with low- and medium-risk levels. Therefore, the evaluation of risk levels other than high and very high can provide a more complete picture of the hotspots. Conclusions The application of input/output-based SLCA on the clothing production supply chain provided a more complete picture of the social hotspots than with traditional process-based SLCA. Some unexpected sectors related to commerce and business appeared as social hotspots in the clothing industry. The study explored some important parameters in applying an input/output-based SLCA. The results show that the cut-off values and definition of hotspots in relation to risk levels can directly influence the results. |
| abstract_unstemmed |
Purpose A cradle-to-gate, input/output-based social life cycle assessment (SLCA) was conducted using the Swedish clothing consumption as a case study. The aim was to investigate the influence of the cut-off rule and the definition of “hotspots” in social hotspot assessment. A second aim was to identify social hotspots of Swedish clothing on a national level. Methods The case study was based on the SLCA methodology provided in the Guidelines for Social Life Cycle Assessment of Products (Benoît and Mazijn 2009). An input/output model was used to define the product system from cradle to gate. The negative social hotspots were evaluated for a set of social indicators that were selected by consumers. The impact assessment was conducted on a sector and country level by using the Social Hotspots Database. The identified sectors of the economy with high and very high levels of risk were listed for each social indicator. Results and discussion The results pinpointed some hotspots throughout the supply chain for Swedish clothing consumption. Some unexpected sectors such as commerce and business services in Bangladesh were identified as important hotspots as well as main sectors in the production phase such as plant fibres, textiles and garments that would be expected also on the bases of a traditional process analysis. A sensitivity analysis on different cut-off values showed the extent to which the choice of cut-off rule can directly affect the results via influence over the number of country-specific sectors (CSSs) in the product system. The influence of the hotspot definition was investigated by evaluating the working hour intensity for low- and medium-risk levels for three different indicators. The results show that for child labour, 92 % of the share of working hours was associated with low- and medium-risk levels. Therefore, the evaluation of risk levels other than high and very high can provide a more complete picture of the hotspots. Conclusions The application of input/output-based SLCA on the clothing production supply chain provided a more complete picture of the social hotspots than with traditional process-based SLCA. Some unexpected sectors related to commerce and business appeared as social hotspots in the clothing industry. The study explored some important parameters in applying an input/output-based SLCA. The results show that the cut-off values and definition of hotspots in relation to risk levels can directly influence the results. |
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Hotspot identification in the clothing industry using social life cycle assessment—opportunities and challenges of input-output modelling |
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| score |
7.400613 |