Social consequences of wood-based innovations: a generic analysis of sectoral differences in Austria

Abstract

Abstract 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.