What should be recycled: An integrated model for product recycling desirability

This research was focused on developing a new scientific approach for prioritising recycling of end-of-life products in a circular economy. To date, product complexity based on the mixture of materials has been used as a predictor of what gets recycled. While the separation of materials that make up a product has been modelled as a measure of product complexity, this does not taken into account the benefits and considerations in recycling products. In this paper, a new agenda and approach to prioritise the recycling of products was developed based on a recycling desirability index. The material mixing complexity measure was inverted into a simplicity index and then extended by modelling the security index for the mix of materials and the technological readiness level of recycling technologies. The extended model is proposed as an integrated measure of the desirability of recycling end-of-life products. From this analysis, an apparent recycling desirability boundary, enabling products to be prioritised for recycling, was developed. This model and analysis can be used as an information source in developing policies and product recycling priorities

Cross-Country Evidence on Motherhood Employment and Wage Gaps: The Role of Work–Family Policies and Their Interaction

Nie dotycz

Integrating ecosystem service bundles and socio-environmental conditions – A national scale analysis from Germany

peer reviewedUnderstanding the relationship and spatial distribution of multiple ecosystem services (ES) in the context of underlying socio-environmental conditions is an essential element of national ecosystem assessments. Here, we use Germany as an example to present a reproducible blueprint approach for mapping and analysing ecosystem service bundles (ESB) and associated socio-environmental gradients. We synthesized spatial indicators of eleven provisioning, regulating and cultural ES in Germany and used the method of self-organizing maps (SOM) to define and map ESBs. Likewise, we collated data from 18 covariates to delineate socio-environmental clusters (SEC). Finally, we used an overlap analysis to characterise the relationship between the spatial configuration of ESBs and co-occurring SECs. We identified and mapped eight types of ESBs that were characterized to varying degrees by provisioning, cultural and regulating/maintenance services. While ESBs dominated by provisioning ES were linked to regions with distinct environmental characteristics, cultural ESBs were associated with areas where environmental and socio-economic gradients had similar importance. Furthermore, spatial stratification of ESBs indicated hot spots where more detailed analysis is needed within national assessments. Our approach can serve as a blueprint for ESB analysis that can be reproduced in other geographical and environmental settings