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Purpose To model the overall social consequences of changing wood utilization, a system perspective is needed that encompasses the entire wood utilization system in a defined region. The aim of this study was to analyze the social performance of wood-based industries in Austria using sector-specific data and to use less disaggregated data to depict social risks in the resource extraction phase. Additionally, the social consequences of innovations in terms of the social performance of a sector and potential side effects on other wood-based industries were analyzed. Methods Differences in the sectoral social performance of forestry and wood-based industries in Austria were analyzed using sectoral data for 11 different social indicators (e.g., occupational injuries, woman in managerial positions) collected at official sites in Austria. To calculate the overall social performance of the sector, sectoral data from Austria need to be combined with data from other sources representing the value chain (e.g., from resource extracting countries). This enables the social consequences of a change to be analyzed by including the social indicators in the system dynamics model WOODSIM. The WOODSIM model depicts the Austrian wood utilization system, allowing the user to model direct and indirect effects of introducing an innovation in a particular industry on the social performance of industries in the wood utilization system. Results and discussion The results show that social risks can differ depending on the sectoral context even within the same country (e.g., occupational injuries in wood harvesting compared to textile production). The most dangerous sectors (in terms of injuries) are forestry and construction (34 and 3 times higher than Austrian average, respectively). Including the risks of resource extraction affects the social performance of the industries. Surprisingly, the median for Austria is 1434 accidents per 100,000 employees, whereas it is only 592 for all countries combined. Modeling the social consequences with system dynamics reveals that some innovations can result in bigger improvements in social performance than others, mostly due to the existence of more globalized value chains. Conclusions This work illustrates the importance of including sectoral information when performing generic social life cycle assessments and models the social consequences of an innovation for the first time using system dynamics modeling. To avoid overestimating positive effects when analyzing consequences, a systems perspective must be taken. Better and more disaggregated data are needed to depict the social performance of sectors more accurately.
Purpose To model the overall social consequences of changing wood utilization, a system perspective is needed that encompasses the entire wood utilization system in a defined region. The aim of this study was to analyze the social performance of wood-based industries in Austria using sector-specific data and to use less disaggregated data to depict social risks in the resource extraction phase. Additionally, the social consequences of innovations in terms of the social performance of a sector and potential side effects on other wood-based industries were analyzed. Methods Differences in the sectoral social performance of forestry and wood-based industries in Austria were analyzed using sectoral data for 11 different social indicators (e.g., occupational injuries, woman in managerial positions) collected at official sites in Austria. To calculate the overall social performance of the sector, sectoral data from Austria need to be combined with data from other sources representing the value chain (e.g., from resource extracting countries). This enables the social consequences of a change to be analyzed by including the social indicators in the system dynamics model WOODSIM. The WOODSIM model depicts the Austrian wood utilization system, allowing the user to model direct and indirect effects of introducing an innovation in a particular industry on the social performance of industries in the wood utilization system. Results and discussion The results show that social risks can differ depending on the sectoral context even within the same country (e.g., occupational injuries in wood harvesting compared to textile production). The most dangerous sectors (in terms of injuries) are forestry and construction (34 and 3 times higher than Austrian average, respectively). Including the risks of resource extraction affects the social performance of the industries. Surprisingly, the median for Austria is 1434 accidents per 100,000 employees, whereas it is only 592 for all countries combined. Modeling the social consequences with system dynamics reveals that some innovations can result in bigger improvements in social performance than others, mostly due to the existence of more globalized value chains. Conclusions This work illustrates the importance of including sectoral information when performing generic social life cycle assessments and models the social consequences of an innovation for the first time using system dynamics modeling. To avoid overestimating positive effects when analyzing consequences, a systems perspective must be taken. Better and more disaggregated data are needed to depict the social performance of sectors more accurately.
Introduction S-LCA has emerged within sustainability assessment in the last 20 years, aiming at assessing the social impacts of products and services. Several improvements on the methodological and theoretical backgrounds were reported in the literature, since its early beginning. However, there are still some gaps that must be filled: lack of consensus on the indicators, methodological limitations, and hegemony of secondary data, among others. Therefore, this research aimed to benefit from one area related to social sciences, named as psychometrics, to develop 14 questionnaires (scales) to collect primary data regarding all worker’s impact subcategories. Method In general terms, the methodological steps were as follows: (1) literature review of the impact subcategories; (2) definition of the constitutive and operational definitions; (3) run focus groups with workers from different economic sectors and positions to deep understand their reality; (4) creating scales’ items; (5) run semantics analysis; (6) run specialists’ analyses; (7) pilot and final application of the scales; and (8) run exploratory factor analysis (EFA) and confirmatory factor analysis (CFA). Results and discussion Five different focus groups were run to better understand each subcategory, and 14 semantics analyses to check the understanding of the scales’ items. Five different groups comprised of four different experts on S-LCA assessed the items’ content. Kappa’s Fleiss indicated from moderate to almost perfect agreement. The scales were applied in two rounds. On the first, EFA was run pointing out to factor retention solution identical or close to the literature review. CFA confirmed the models proposed with adequate adjustment indices. The analyses have shown that all 14 scales indicated to have evidence of content validity and validity based on the internal structure. The reliability coefficients relating to the indicators appointed to a high precision on the measurement. Conclusions This study contributes to overcoming some of the current theoretical and methodological gaps in S-LCA. Practitioners can benefit from 14 scales to collect primary data regarding worker in a reliable, scientific, and confidential way. Data can be used in both types of impact assessment methods. The scales may also support other research initiatives that aim at studying and improving working conditions for all workers, from any economic sector.
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